From Cursor to Supabase: 45+ MCP Tools Guide (2025 List)

Ever built something cool only to realize it couldn’t scale, connect, or even explain itself to other tools? Yeah…same. That’s where model context protocols (MCPs) come in. They’re not some trendy acronym (maybe just a bit). 

They’re the missing piece for modern AI-native apps.

MCPs are how your app says, “Here’s what I am, here’s what I know, and here’s what I can do”—in a way that models, humans, and other systems can all understand. They help apps speak a common language.

If you’ve been poking around tools like Cursor or Supabase lately, you’ve probably felt it—apps are changing. They’re more dynamic, more modular, and powered by language models that want structured, contextual info. That’s what MCPs provide.

But here’s the thing:

• You don’t need to become a protocol engineer.
• You just need the right starting point.

This guide (think of it as an MCP directory) gives you 45+ real-world MCP-enabled apps—from development toolkits to sales platforms like Generect with its own MCP contribution. We’ll make it a living list of what’s possible in 2025 and how you can start building with it today and tomorrow.

We’ll walk you through:

• What MCPs are (in plain English with the link to the comprehensive guide)
• How they’re changing how we build model-aware software
• Real apps already using them—across devtools, data, ops, and more

Along the way, you’ll see quick breakdowns, links to source code or templates, and notes from builders in the field. We’ve kept it practical—no fluff, no filler.

What is MCP? (super-simple)

Let’s keep this easy: MCP stands for Model Context Protocol. It’s just a smart way to tell a language model (like GPT) what your app is, what it does, and how it works—without repeating yourself a hundred times.

Imagine your app is joining a group chat with a bunch of other tools and models. MCP is your app’s intro message. It says:

• “Hey, I’m a task manager.”
• “Here’s how my data’s structured.”
• “Here’s what users can do with me.”
• “Here’s how to call my API.”

That’s it. No magic. Just structured, shareable context. But, why does it matter?

Language models are powerful, but clueless by default. They don’t know your app. They don’t remember your schema. And they definitely don’t know what your button labeled “Do Magic” actually does.

That’s where MCP helps. It acts like a cheat sheet for the model, telling it:

• What your app knows
• How your app behaves
• What actions it supports

When you give a model this kind of context, it becomes way more useful. You can:

• Let users ask natural questions about your app (“What’s overdue in Project A?”)
• Automate workflows that touch your backend
• Build assistants or agents that actually know how your stuff works

Okay, but what is an MCP in practice? And it’s up next: real apps using MCPs today, and we’ll start with the development tools.Yet, we’ll start with the announcement…

Development MCP tools

Let’s talk dev tools first, because this is where MCP really starts to shine. You’re not just coding anymore. You’re coding with a model that knows your context. 

The tools you already love are evolving to work side-by-side with AI, and MCP is what makes that partnership possible. So, when speed meets context-aware coding…

Cursor

…take Cursor, for example. It’s an IDE built for developers who want more than autocomplete. With MCP baked in, Cursor gives AI assistants a deep understanding of your codebase. 

That means smarter suggestions, helpful refactors, and even natural language debugging. You can just ask, “What’s wrong with this function?” and it gets it, because the model has the context it needs. 

Soon, you’ll be able to connect Cursor with other tools too, making AI part of your end-to-end dev workflow.

So, what exactly can MCP do inside Cursor? Let’s break it down.

• Cursor MCP gives AI direct access to your codebase. 
• It helps with refactoring, debugging, and code generation using natural language.

Also, you might want a playground that runs in your browser and scales on demand…

Replit

…and get it over in Replit, where things are just as exciting. You can write, run, and debug code—all in the browser—with an AI that’s aware of your environment. 

Thanks to MCP, the model doesn’t just spit out code. It understands the file it’s editing, the terminal output it’s reading, and what you’re trying to build. And we’re not far off from AI scaffolding entire projects and wiring up databases for you, right inside Replit.

Here’s how that all plays out when you’re actually coding in Replit.

• It lets AI write, run, and debug code in-browser. 
• It supports real-time feedback from the coding environment.

Need local-first performance with multiplayer vibes? Slide into Zed.

Zed

Zed adds collaboration into the mix. It’s a sleek, multiplayer code editor that lets you and your teammates build together. With MCP, AI becomes a third teammate—one that helps you navigate code, edit smarter, and maybe even mediate disagreements over function names. 

In the future, it could track tasks, suggest improvements, or act as an ever-present pair programmer.

Wondering what that looks like in practice? Here’s what MCP unlocks in Zed.

• It enables AI-assisted editing and navigation in your code. 
• It helps during live collaboration sessions.

And once your codebase grows, Sourcegraph helps you search it like a boss.

Sourcegraph

Then there’s Sourcegraph, which turns AI into a code-search powerhouse. Instead of just grepping your repo, you can ask, “Where do we use this pattern?” and get a smart, context-aware answer. 

It’s already helping teams find bugs and refactor faster. Down the line, it could keep your docs up to date or hook into your CI pipeline to catch issues before they ship.

Let’s look at what you can actually do with MCP inside Sourcegraph.

• It allows AI to search and understand code across repos. 
• It helps detect patterns and suggest refactors.

Testing APIs? Apidog lets you craft, mock, and document in one swoop.

Apidog

APIs aren’t left out either. Tools like Apidog use MCP to let AI test and document APIs automatically. 

You don’t need to write the test cases yourself—it can figure out what your endpoints do and how to call them. That’s real productivity. And it’ll only get better as these tools start connecting with others to create full testing pipelines powered by AI.

Here’s how MCP helps Apidog work smarter—without extra setup from you.

• It connects APIs to AI. 
• It lets assistants run API tests, validate responses, and generate docs automatically.

No SQL? No problem. AI2SQL writes your queries like it’s reading your mind.

AI2SQL

AI2SQL is another fun one. You write in English, and it spits out the right SQL. It’s fast, accurate, and surprisingly helpful when you can’t remember the exact syntax. 

Because it uses MCP to understand your schema, it doesn’t just guess = it knows. Soon, this kind of interface could connect directly with BI tools and generate live reports for you.

So. how does this help when you’re actually using it? Here’s what it can do.

• It turns natural language into accurate SQL queries. 
• It makes database access fast and beginner-friendly.

Of course, you’ll need solid foundations — whether local or production-grade.

PostgreSQL & SQLite

Let’s not forget the databases themselves. Both PostgreSQL and SQLite are becoming way more accessible when you plug them into MCP workflows. Suddenly, you can ask things like, “What were our top sales regions last month?” and the model will generate the query and pull the data—right there. 

You don’t need to touch SQL unless you want to. And down the line, expect tighter integrations with dashboards, apps, and even mobile devices.

Here’s what you’ll notice once MCP is added to PostgreSQL and SQLite.

• It lets AI run and analyze SQL queries. 
• It helps with data retrieval and manipulation using plain language.

Code’s written, now let’s version, review, and collaborate at scale.

GitHub & GitLab

GitHub and GitLab are getting smarter too. With MCP, your AI assistant can understand issues, pull requests, and repo structure. That means better code reviews, faster merges, and maybe even predictive insights—like catching risky changes before they happen. GitLab’s already moving in that direction, with AI helping manage issues and pipelines like a superpowered project manager.

Here’s what AI can do inside GitHub and GitLab once MCP is part of the stack.

• It enables AI to review code, manage issues, and open pull requests. 
• GitHubMCP helps automate repo tasks.
• It lets AI handle merge requests and track project work. 
• It makes issue management easier through natural prompts.

Oops? When things break, Sentry’s got eyes on it before you even notice.

Sentry

Finally, there’s Sentry. With MCP support, the model can pull in error data, link it to recent code changes, and help you figure out what’s going wrong—and fast. 

Soon, MCP program might even suggest fixes or integrate directly with your alerting tools to cut down on fire drills.

Here’s what that means for you when debugging inside Sentry.

• It gives AI access to error logs and performance data. 
• It helps identify bugs and guide debugging.

Each of these tools brings something unique to the table, but they all share one thing: with MCP, AI doesn’t just assist—it understands. And that changes everything.

But it doesn’t stop at writing code. Once you’ve built something, you need to connect it all together. That’s where automation tools come in—and with MCP, they’re smarter than ever.

Automation & orchestration

If you’ve ever found yourself stitching together tools, APIs, and scripts just to move data from point A to B, you’re not alone. That’s where automation and orchestration tools come in. 

And when you bring MCP into the picture, you stop just automating and start coordinating with context-aware AI that actually understands what’s going on.

n8n

Start with n8n, a flexible workflow automation tool. It now supports MCP through special nodes, which means AI assistants can jump into your workflows and actively run the show. 

You can ask them to pull in data, transform it, and send it off to the right place—without needing to script every little thing. And as AI models get better at decision-making, you’ll see n8n becoming more than a task-runner (think about it as an advanced Zapier MCP server alternative). 

It’ll be more like a behind-the-scenes operations manager, making smart calls on the fly.

Let’s see how MCP makes n8n more flexible, hands-off, and context-aware.

• It lets AI trigger and manage automated workflows. 
• It helps move data between apps with context.

Need an AI that fits inside your stack? SpinAI keeps it nimble and private.Yet, let’s take a short pause…

SpinAI

Then there’s SpinAI, a TypeScript framework built from the ground up to support AI agents. It’s fully open-source and plays nicely with other MCP-enabled tools. 

You can build agents that aren’t just reactive—they’re observant. They can make decisions, watch for issues, and interact with other systems like they’ve been trained on your stack for months.

Over time, SpinAI’s going to get even better at helping you track and debug your agents’ behavior in real time, so you’re never flying blind.

Here’s how it works when you’re building with SpinAI.

• It allows AI to create and manage agent logic in TypeScript. 
• It enables smart agents that can observe and act.

Want to test it all together? Containerize your world with Docker.

Docker

Docker is already a staple for developers, but with MCP-powered interfaces, AI can now help manage containers, networks, and images. You don’t need to memorize command-line flags anymore—just describe what you want, and the AI can spin it up. It’s especially useful for repeatable environments or when you need to prototype fast. 

As MCP support grows, expect these systems to tie into orchestration tools and bring more secure, scalable container management into your AI workflows.

Here’s what that looks like when you use Docker with MCP on board.

• It enables AI to create, manage, and monitor containers. 
• Docker MCP makes container operations voice-controllable.

And if you’re going big, Kubernetes orchestrates it like a maestro.

Kubernetes

Speaking of orchestration, Kubernetes is also joining the MCP era. With the right setup, AI can help scale your apps, manage pods, and even tweak configurations. It understands what’s running and can suggest changes or rollouts, keeping things optimized without manual babysitting. 

Eventually, you’ll see AI help balance resources across your cluster, or even adjust how microservices talk to each other—all based on real-time context.

Here’s how MCP changes how you scale and run things in Kubernetes.

• It lets AI interact with Kubernetes clusters. 
• It helps scale apps, manage configs, and monitor services.

Stepping back to map ideas? Think Tool gives your thoughts a canvas.

Think Tool

Now, zooming out a bit, there’s Think Tool—a neat layer that adds decision-making into the mix. Instead of just running tasks, it helps AI agents think through problems. Want to plan something complex or analyze multiple scenarios?

Think Tool gives the model the mental space to do that. It’s like giving your automation a strategic brain, not just arms and legs.

Let’s look at how that shows up when using Think Tool in real-world tasks.

• It provides reasoning tools for AI. 
• It helps make decisions, plan steps, and analyze scenarios.

Then break them down step by step — for clarity that scales.

Sequential Thinking

And to tie it all together, there’s Sequential Thinking. This one’s all about helping AI reason step-by-step. You’re not just handing the model a task and hoping it gets it right. Instead, it works through the problem methodically, adjusting its thinking as it goes. 

That’s especially powerful when the problem isn’t obvious or the solution needs a bit of creativity. As this evolves, AI will be able to remember past thought processes and explore alternative paths—just like a human brainstorming session.

So how does this make things easier? Here’s what MCP actually enables.

• It helps AI reason step-by-step. 
• It breaks big tasks into smaller, more manageable pieces.

Sometimes, your data isn’t in a database. It’s on the web. No matter if you’re scraping, searching, or browsing, MCP lets AI assistants explore and understand the internet like never before.

Web, browser & search

Let’s face it—sometimes, the data you need isn’t in an API or a database. It’s sitting behind a login, tucked into a web page, or buried in search results. That’s where browser automation and web-aware tools come in. 

And with MCP in the mix, AI can now reach out, click through, search, scrape, and understand the web just like a human would—only faster.

Puppeteer

Model context protocol tools like Puppeteer make this possible. It lets AI assistants navigate pages, fill out forms, click buttons, and scrape content, all through browser control. Thanks to MCP, the AI doesn’t just follow a script—it understands the context of what it’s doing.

So instead of telling it exactly where to click, you can ask it to “log in and grab today’s prices.” 

Over time, you’ll see it get even smarter at dealing with dynamic content, popups, and those annoying JavaScript-heavy sites.

Let’s break down what browser tools MCP adds when AI’s running Puppeteer behind the scenes.

• It enables browser automation through AI. 
• It helps with scraping, form filling, and navigation.

Digging up data? Brave’s private search gives you fast, clean answers.

Brave Search

When it comes to search, Brave Search steps in with real-time access. You can use it to feed fresh results directly into your AI workflows. That means your assistant can answer questions with up-to-date information, not just what it was trained on months ago. 

MCP connects the dots so the model can ask the right questions, interpret results, and even apply filters when needed. It’s like giving your AI a built-in search engine that speaks its language.

Here’s how Brave Search gets even more useful with MCP plugged in.

• It lets AI pull fresh search results. 
• It helps with up-to-date queries inside apps and workflows.

Need structured results from any engine? SerpApi pulls it all in, parsed.

SerpApi

SerpApi takes that one step further. It gives AI structured access to search engines—Google, Bing, and more. So instead of just scraping the page, your assistant actually knows where the results are, how they’re structured, and what to do with them. 

Whether you’re building a real-time news aggregator or an app that needs to understand market trends, this kind of search becomes a game changer. And it’s only going to get better as models learn to analyze and summarize search results in context.

So how does this structured search actually show up in your flow? Here’s what happens.

• It allows AI to perform structured search queries. 
• It helps fetch results from search engines accurately.

For deeper grabs, Fetch helps bring the web straight into your logic.

Fetch

Need raw content from a web page? That’s where Fetch comes in. 

It grabs the page, strips the clutter, and converts everything into markdown that a language model can actually digest. You don’t need to write a custom parser or deal with messy HTML. Just tell the AI what site to pull from, and this MCP program gets clean, readable content, ready to summarize or analyze. 

Soon, Fetch will support richer content types and tighter integrations with summarization model context protocol tools, making it even easier to turn the open web into structured knowledge.

Here’s how Fetch puts raw content into AI’s hands—clean and usable.

• It fetches and converts web pages into markdown. 
• It makes them easy for language models to read.

Or run full-browser automation at scale — headless and ready.

Browserbase

And if you want to scale this kind of automation, check out Browserbase. It’s a cloud-based browser you control through MCP. That means your AI can spin up browser sessions in the cloud, interact with pages, and fetch data—without running anything locally. 

You’re no longer limited by your machine or bandwidth. It works great for scraping, testing, or anything that needs a real browser behind it. And it’s on track to support even more sessions at once, with better monitoring and logging so you can keep an eye on what’s happening.

Let’s take a look at how Browserbase changes the game with MCP support.

• It gives AI a cloud-based browser.
• It lets it run scripts and interact with web pages remotely.

Of course, not everything lives in the cloud. Many apps still rely on local files, notes, and folders. Let’s look at how MCP brings structure and intelligence to your files—wherever they’re stored.

File systems & storage

Now let’s talk files—because even in the era of AI, we’re still working with docs, notes, and folders. 

The difference is that with MCP, your AI assistant can finally understand and manage those files like a real teammate, not just a fancy text generator.

Google Drive

Take Google Drive, for example. It got its MCP extension, and with its support, AI can dive into your Drive, pull out the exact document you need, summarize it, and even organize your files based on what you’re working on.

Instead of digging through folders, you can just say, “Find the latest product spec and summarize it,” and the AI handles it. Down the line, it could even highlight important updates, suggest edits, or sync files across MCP tools you’re already using.

Here’s what you can actually do with MCP and Google Drive working together.

• It lets AI find, summarize, and organize files. 
• It helps manage your Drive with voice or prompts.

Storing all that output? Or go local — sometimes the simplest storage is still the smartest.

Filesystem

Local file systems are getting the same upgrade. Thanks to secure MCP integrations, AI can read from and write to specific directories on your machine, with strict controls in place to keep things safe. That means you can let it fetch logs, scan configs, or save generated code without worrying about it poking around where it shouldn’t. 

It’s a powerful way to blend automation with local development. 

And as more file formats and systems get supported, you’ll be able to do even more—from managing large data sets to deploying code with version control baked in.

Let’s walk through how this makes your local files accessible in a smart, secure way.

• It allows secure local file access. 
• It helps AI read and write files safely within limits.

Need to link thoughts, not just files? Obsidian builds your second brain.

Obsidian Markdown Notes

If you’re a note-taker or second-brain builder, you’re gonna love what’s happening with Obsidian. 

With MCP support, AI assistants can search your vaults, read and write notes, follow internal links, and even manage metadata like tags and backlinks. You don’t need to remember where you stashed that one insight from three months ago—just ask, and it’s there. 

In the future, you’ll be able to explore ideas in a whole new way, with the AI spotting connections between notes and surfacing patterns you might’ve missed.

Here’s how your second brain gets even better with MCP application and AI working together.

• It gives AI access to your notes. 
• It helps find links, manage tags, and surface insights.

With your data in place, it’s time to get things done. From team chats to task lists, MCP is now powering the tools you use to stay organized and collaborate more effectively. Still, we have something to say here…

Productivity & communication

Managing tasks, chatting with your team, and keeping track of everything across a dozen tools can get (let’s be honest) messy fast. 

With MCP in the mix, your AI assistant isn’t just another inbox—it actually helps you do the work. It jumps in, manages tools, keeps context, and helps you stay on top of things without juggling tabs all day.

Slack

Start with Slack. It’s already the heartbeat of many teams, but now, with MCP, AI can actually join the conversation. It can send updates, automate messages, spin up channels, and help manage workflows right from inside your workspace. You could say something like “summarize what happened in #dev” and get a clear, useful reply. 

Soon, the Slack MCP server will be able to do more than just repeat—it’ll understand tone, context, and help surface what actually matters.

Let’s see how AI can help you inside Slack with almost zero effort.

• It lets AI send messages, manage channels, and track tasks. 
• It automates team updates in Slack.

Next step? Action it. Todoist keeps things flowing from note to done.

Todoist

Over in Todoist, the same kind of intelligence applies to tasks. Thanks to MCP, you can create, check off, update, or even delete tasks just by asking. You don’t need to open the app or touch a single button.

It feels like telling a real assistant, “Add a reminder for tomorrow to follow up on the design review,” and having it done instantly. 

And once more tools get tied in, you’ll be able to manage tasks across apps in one smooth flow (and let’s also wait for Jira MCP—should be awesome!).

Here’s what it feels like when you bring MCP into Todoist.

• It enables natural language task creation. 
• It lets AI manage your to-dos directly.

Need to pin a place? Whether geo or context, Google Maps gives direction.

Google Maps

Now think about how often you check locations, routes, or addresses. Google Maps gets way more useful when it’s part of an MCP-connected system. AI can look up places, get directions, or fetch details about a location without you needing to dig. 

You could be planning a trip, running a delivery route, or researching a new store location—the AI helps you navigate that context in real time. 

Yet, the real magic will happen when it connects with other tools too, blending location data into schedules, logistics, or team planning (probably with Gmail MCP, we’ll see).

So how does that play out with AI inside Google Maps? Let’s see.

• It lets AI pull location data and directions. 
• It helps with routing and planning tasks.

And to keep it all connected? Smart Memory bridges tools and context.

Memory

Memory is another one to watch—it’s like long-term brain power for your AI. 

With a persistent graph-based memory system, your assistant doesn’t just respond—it remembers. That quick note you mentioned last week? It’ll bring it back when it’s relevant. It understands ongoing projects, team dynamics, even your preferences. 

Over time, this gives AI the ability to actually carry context forward, helping you make decisions that build on everything you’ve already done or said.

Here’s how your assistant starts to feel like it actually knows you.

• It gives AI long-term memory. 
• It lets it remember past actions and reference them later.

Shipping product? Linear handles issues without slowing you down.

Linear

Finally, Linear is where your AI steps into project management. Through MCP, it can create and update issues, track sprints, and even help manage teams. You could ask, “What’s left in this sprint?” or “Create a new issue for that UI bug,” and it’s handled. 

As the data grows, the AI will be able to give insights too—like spotting blockers, analyzing team velocity, or suggesting changes to help your team move faster.

Let’s look at what that means for your team when you’re using Linear.

• It lets AI track issues, sprints, and team progress. 
• It helps manage dev work in real time.

Once the work’s in motion, you’ll want to track how it’s performing—and how it’s earning. This next section shows how MCP gives AI assistants access to payment systems and analytics tools to help you stay on top of both.

Payments & analytics

Once your app is live, you need to know two things: how it’s making money and how well it’s performing. With MCP, your AI assistant can now help with both—no dashboard-hopping, no SQL wrangling, and no waiting for weekly reports. 

Just answers, right when you need them. Let’s take a look.

Stripe

Start with Stripe. Through MCP, AI can directly interact with your Stripe data. It can pull up recent transactions, look into customer histories, and even generate financial summaries without needing you to dig into the dashboard.

Say something like “show me this week’s revenue by product,” and it’ll pull that up in seconds. 

As MCP evolves, this setup won’t just report—it’ll help detect unusual patterns, flag potential fraud, and tailor experiences based on how customers actually pay and interact with your product.

Here’s how AI helps you handle money and metrics faster in Stripe.

• Stripe MCP allows AI to generate reports and track transactions.
• It supports finance tasks inside apps.

Need more than just crash reports? Raygun surfaces the full user impact.

Raygun

On the performance side, model context protocol tools like Raygun are stepping in to make error tracking and app monitoring way more intelligent. When hooked into MCP, your AI can read crash logs, track performance issues, and even highlight trends over time. You won’t need to hunt down bugs manually—it can guide you straight to the line of code that’s causing trouble. 

Over time, you can expect AI to not only spot crashes, but predict and prevent them before they cause damage.

So what does MCP application bring to the table here? Let’s take a look.

• It lets AI analyze crash logs and performance data. 
• It supports real-time error tracking.

And when it’s time to query logs like it’s SQL—Axiom has your back.

Axiom

Axiom brings observability into the conversation. With MCP, you can ask natural language questions like “what happened right before that spike in latency?” and get a direct answer pulled from your logs and event streams. The AI understands the structure of the data and can surface key insights fast. It’s like having a superpowered analyst on call. 

And it’ll only get better as predictive features roll out and connect with other monitoring tools in your stack.

Here’s how that turns your logs into something you can actually talk to.

• It enables natural language queries over logs.
• It helps AI analyze and explain event data.

Real-time data needs real-time tools — Tinybird’s built for the now.

Tinybird

Then there’s Tinybird, which gives you real-time access to ClickHouse data using MCP. This means you can query massive datasets quickly and actually make sense of the results, even if you’re not a data engineer. 

Ask your AI assistant to track user behavior, analyze a feature rollout, or summarize metrics across time—and it’ll handle the querying and formatting for you. 

Soon, you’ll be able to auto-generate dashboards and plug these insights directly into decisions, without waiting for analytics reviews.

Let’s see how MCP makes Tinybird way more interactive and real-time.

• It lets AI query live data fast.
• It helps with real-time dashboards and data summaries.

Data isn’t just numbers—it’s context, knowledge, and insight. MCP helps AI tap into everything from databases to encyclopedias. Here’s how it all comes together.

Data & knowledge sources

At the core of any smart app is data. But here’s the thing—having data isn’t enough anymore. You need ways to access it, understand it, and use it in real time. 

That’s exactly where MCP makes a difference. 

This MCP application connects your AI assistant to data sources and knowledge bases in a way that actually makes them useful, no matter if it’s querying a database, solving a math problem, or summarizing research. And we’ll start with the MCP Supabase-based.

Supabase

Let’s start with Supabase. If you’ve used it before, you already know it’s a powerful Firebase alternative with real-time features and a solid Postgres foundation. 

Now, with MCP in the picture, your AI can talk directly to your Supabase instance. That means querying tables, managing users, or handling auth flows without jumping between tabs or writing boilerplate. You can say things like “create a new user table with an email index,” and it’ll just happen. 

And soon, you’ll be able to collaborate in real time—multiple users, one assistant, working on the same database together.

Here’s how that plays out when you’re working with Supabase and AI together.

• Supabase MCP allows AI to query, manage, and auth Supabase data. 
• It helps build full-stack apps with natural language.

Math-heavy lifting? Wolfram|Alpha brings the computation power.

Wolfram|Alpha

For heavier computation and deeper questions, Wolfram|Alpha steps in. It brings serious math and scientific reasoning into your workflow. With MCP support, your assistant can now perform complex calculations, generate plots, and even explain formulas using Wolfram’s powerful engine. 

No matter if you’re a developer, a student, or just solving something tricky, it’s like having a pocket calculator that speaks fluent English. In the future, expect even tighter integration with MCP tools that can turn those results into visualizations or tutoring experiences.

Let’s see how AI uses Wolfram|Alpha to go from “Huh?” to “Aha!”

• It lets AI perform calculations and answer complex questions. 
• It makes technical queries interactive.

Or maybe just a quick context check — Wikipedia to the rescue.

Wikipedia

When you need factual content, Wikipedia becomes your AI’s go-to. MCP lets assistants pull summaries, dive into related topics, and surface the right information fast. Instead of digging through pages, you just ask, and the answer shows up—clean, concise, and sourced. 

You’ll soon see more advanced MCP use cases too, like building knowledge graphs that connect Wikipedia with your own internal data, or even monitoring article updates for breaking changes.

Here’s how your AI gets smarter answers—fast—with Wikipedia in the mix.

• It allows AI to summarize articles and fetch factual data. 
• It helps bring verified info into workflows.

Searching enterprise-scale knowledge? AWS keeps it secure and smart.

AWS KB Retrieval

If you’re working in the AWS ecosystem, the AWS Knowledge Base becomes a key resource. Through AWS MCP and Bedrock Agent Runtime, AI assistants can now pull documentation, understand how services work, and even help configure infrastructure. 

This turns static docs into something interactive—you ask a question, and the AI replies with the exact snippet or guidance you need. Eventually, this could lead to auto-configured cloud setups based on your goals and usage patterns.

Let’s see how MCP helps AI make sense of AWS without digging through docs.

• It gives AI access to AWS docs and knowledge. 
• It helps answer cloud setup and config questions.

Got data everywhere? Snowflake brings it together for powerful querying.

Snowflake

Snowflake, too, is getting the MCP treatment. Your AI can now tap into your data warehouse to run queries, check schemas, and analyze usage patterns. You don’t need to remember SQL syntax or wait on someone from data engineering. 

Just ask what you need. As integrations mature, you’ll be able to connect real-time streams and build automated governance into your workflows—without slowing down development.

Here’s how AI can start working directly with your warehouse—without the wait.

• It lets AI query Snowflake and manage schemas. 
• It helps with enterprise data tasks and dashboards.

And when search goes vector-based, Qdrant and Weaviate make it blazing fast.

Qdrant & Weaviate

For smarter, more semantic interactions, vector databases like Qdrant and Weaviate are key players. With MCP, your AI can store memory, search by meaning, and surface the most relevant chunks of information—whether it’s from docs, chats, or user feedback. This is where apps start to feel truly intelligent. 

They remember your preferences, respond with context, and evolve over time. In Weaviate’s case, that even extends to RAG workflows where the assistant retrieves and generates answers on the fly. 

Down the line, these systems will fuel collaborative agents that solve bigger, more domain-specific problems across finance, healthcare, and beyond.

Let’s look at how these tools help AI remember, relate, and respond better.

• It enables vector search and memory recall. 
• It helps AI personalize responses based on stored info.
• It lets AI retrieve and generate responses using vector data.

A unified gateway to multiple sources? Search1API is your one-stop search shop.

Search1API

And to keep all of this connected to the live web, tools like Search1API let your AI crawl, search, and pull structured or unstructured content from the internet through a unified MCP interface. 

Whether you’re tracking trends, monitoring sentiment, or just grabbing the latest headlines, it brings a real-time pulse to your AI workflows. Eventually, it’ll even help generate content based on what’s happening online right now—automated, informed, and relevant.

So what’s that look like in action? Here’s how Search1API keeps your AI current.

• It allows AI to crawl and search the web in real time. 
• It helps track trends and gather content.

Behind the scenes, there are powerful platforms making all this possible. MCP turns these into more than model hosts—it makes them interactive environments. Let’s explore how.

AI platforms & frameworks

As AI becomes more capable, the platforms around it are getting smarter too. This MCP software isn’t just powering models—it’s giving them context, access, and control. 

That means your assistants don’t just answer questions anymore. They do things. They interact with your files, your apps, your systems. And suddenly, AI becomes part of your everyday workflow—not just a separate tool.

Claude Desktop

Take Claude Desktop as an example. With MCP integration, Claude can now interact directly with files and apps on your computer. 

That means you can ask it to open a folder, move some documents, find a file, or even write and edit something locally. It’s fast, secure, and feels a lot like having a smart desktop assistant that understands exactly what you’re trying to do. And as integration expands,

Claude will start working with even more of your local tools—while staying tightly controlled by permissions you define.

Here’s how Claude starts helping with everyday tasks on your computer.

• It enables file search and manipulation locally. 
• It lets Claude act as a desktop AI assistant.

Or extend Copilot’s power into your workflows — now you’re coding with copilots.

Microsoft Copilot Studio

Over in the Microsoft ecosystem, Copilot Studio is opening up even more possibilities. With MCP, you can now extend intelligent agents in Copilot using your own servers, tools, and APIs. So instead of relying only on what’s built in, you can define exactly what your Copilot can do—no matter if that’s connecting to your internal CRM, pulling custom analytics, or triggering workflows unique to your business.

It’s powerful, and soon you’ll have even more control over how these integrations are built and managed inside the Studio interface.

Let’s break down what you can actually do inside Copilot Studio with MCP.

• It lets Copilot talk to your tools using MCP. 
• It helps you define new actions for intelligent agents.

And for AI-generated visuals? EverArt paints your vision into reality.

EverArt

And if you’re working in creative spaces, EverArt is a fun one to explore. It uses MCP-compatible interfaces to let AI generate images based on prompts and parameters. So you can ask for a design, tweak it on the fly, and get visual content that matches your vision—all from a single prompt. It’s simple now, but the creative potential is huge. 

We’re heading toward a world where you’ll be able to edit, remix, and generate visuals with AI tools that work directly alongside your other content platforms, all through a shared protocol.

Here’s what happens when you bring MCP into your creative flow.

• It allows AI to generate images from prompts. 
• It supports creative tasks inside design workflows.

And finally, let’s bring this into the world of sales. From lead generation to CRM automation, MCP is giving sales teams new ways to connect, convert, and close.

Sales MCP tools

Sales is all about timing, context, and knowing who to talk to—and when. With MCP, your AI assistant can finally support your sales process in a meaningful way. 

Instead of just handling admin tasks, this MCP software can plug into real tools, understand what’s going on, and help you act fast on the right leads.

Generect

At Generect, we built our MCP integration to change how lead generation and cold outreach actually work. Instead of handing you stale lists or generic contacts, we give your AI assistant real-time access to qualified, sales-ready leads—complete with intent signals, triggers, and everything you need to reach out at the perfect moment.

With MCP, your assistant isn’t just helping—it’s prospecting, filtering, and prioritizing leads like a pro.

What MCP will add to Generect:

1. Your AI can search live, up-to-date lead databases without writing a single query.
2. It can surface leads that show real buying intent based on trigger events or behavioral signals.
3. You can ask it for leads in a specific industry, region, or role—and get tailored results instantly.
4. It can prioritize leads by score, activity level, or custom rules you define.
5. It automatically pulls in company info, contact details, and relevant notes for every lead.
6. You can run natural-language prompts like “find VPs of Sales in SaaS who recently changed jobs.”
7. It connects to your outreach tools, helping prep messages or even schedule that first email.
8. It gives you context—why this lead matters, what changed, and what you should say next.

Your users are here — HubSpot helps you connect, nurture, and grow.

HubSpot

If you’re using HubSpot, MCP takes it a step further. Your assistant can now talk to your CRM. It can create new contacts, look up a company’s history, review activity logs, and pull engagement data—all on command. 

You don’t have to open the HubSpot dashboard or remember which tab had that one note from last quarter. You just ask, and it’s there. And as the integration deepens, you’ll be able to manage deals, trigger campaigns, and generate reports—without lifting a finger.

Here’s how AI turns HubSpot into a real-time, responsive CRM assistant.

• No official HubSpot MCP integration yet, but when it’ll be available:
  • It lets AI interact with CRM contacts and companies. 
  • It helps pull engagement history and manage leads.

And when you scale up? Salesforce turns data into relationships that last.

Salesforce

Salesforce is a bit different. Right now, there’s no official MCP integration. But that’s changing fast. Once custom MCP servers roll out, your assistant will be able to access Salesforce data too—contacts, opportunities, workflows, the whole stack. Imagine syncing sales data across your tools, running forecasts, or handling follow-ups automatically, all through a single assistant. 

That kind of automation isn’t far off, and it’s going to be a huge unlock for teams already deep in the Salesforce ecosystem.

MCP support isn’t live yet—but here’s what’s coming when it is.

• No official Salesforce MCP integration yet, but it’s all about future support that could unlock full CRM access and automation.

From prospecting leads to closing deals, it’s clear that MCP is already helping sales teams work smarter. But sales is just one part of the bigger picture. 

When you step back and look at all these MCP tools—from dev environments to CRMs—you start to see how powerful this ecosystem really is.

So let’s pull it all together.

Wrapping up

By now, you’ve seen just how far MCP has come—and where it’s headed. What started as a way to give AI a little more structure is quickly becoming the foundation for building smarter, more connected tools. From coding assistants that understand your entire codebase, to AI agents that manage sales pipelines or analyze logs in real time, the possibilities are growing fast.

If there’s one takeaway here, it’s this: you don’t need to rebuild everything from scratch to use MCP. Most tools are already adding support. Many workflows just need a few endpoints or a simple config. 

And once you get your assistant talking to your stack, things click. You’ll start automating faster. You’ll solve problems quicker. You’ll move from task-based tools to systems that actually understand what you’re trying to do.

So no matter if you’re hacking something together in Cursor, running queries in Supabase, or managing tasks through Slack or Todoist, you’re already part of this shift. 

MCP just gives it all a shared language.

If you’re curious where to start, pick a tool you already use, find its MCP docs or server, and plug it in. Build one small thing. Try a task you used to do manually. Then let it grow from there.

You’re not just exploring a trend—you’re building with the next layer of the web. One where your MCP tools talk, your data flows, and your AI truly understands the context.

And that’s where the magic begins.

P.S. By the way—if you’re excited about where MCP is going, remember that Generect has a waitlist open for folks who are passionate about this stuff. If that’s you, jump in and join the waitlist.