9 MCP Projects for AI Power Users

The Model Context Protocol (MCP) has emerged as a game-changing standard for connecting large language models with external tools, data sources, and capabilities. Often compared to USB-C, which provides standardized physical connections, MCP provides a universal interface for AI model tool connections. This enables integration between AI assistants and the digital world around them (when it works as expected). 

For power users looking to supercharge their AI workflows, these nine MCP projects represent one slice of what's possible with today's AI assistants. From debugging code to orchestrating complex agentic workflows, these tools transform your favorite LLM from a conversational assistant into a versatile collaborator capable of interacting with the digital world more independently.

Let's dive into this multiverse of MCP servers and explore how advanced AI users might make the most of them.

Developer-focused MCP servers

1. Claude Debugs for You by Jason McGhee (297 ⭐ on Github)

Have you ever wished your AI assistant could not just suggest code fixes but actually debug alongside you? Claude Debugs for You brings this capability to life, creating a bridge between AI assistants and VS Code's debugging capabilities.

External technologies:

• VS Code Extension API

• Node.js

• Zod (for schema validation)

• Server-Sent Events (SSE)

Configuration requirements:

• VS Code Extension installation

• MCP client configuration (stdio or SSE)

• Port configuration (default: 4711)

This extension represents a fundamental shift in how AI can assist with debugging. Instead of just suggesting what might be wrong – at best given raw logs and output from your code – now your LLM can actively participate in the debugging session, setting breakpoints, stepping through code execution, and evaluating expressions to identify issues. It's language-agnostic and designed to work with any programming language that supports console debugging in VS Code.

The developer aptly calls this "Vibe Debugging" - a playful term for the integration of AI into the debugging workflow. For developers working with complex codebases, having your LLM as a debugging partner provides an additional layer of expertise to spot patterns and issues that might be missed.

2. Playwright Click Recorder by Ashish Bansal (10 ⭐ on Github)

Several playwright MCP servers have been made, including one official implementation. This repo distinguishes itself by offering a `record-interactions` tool, allowing the human user to create a re-useable series of clicks for the LLM to emulate in the future. 

External technologies:

• Playwright (browser automation framework)

• React

• Node.js

• happy-dom (headless DOM implementation)

• Tailwind CSS

Configuration requirements: No API keys needed - designed to work with locally running browsers.

Every Playwright MCP attempts to solve a critical limitation in AI-assisted browser usage – the inability to see and interact with the actual DOM natively. Without this context, AI assistants are forced to make educated guesses about page elements and structure, often leading to broken test scripts. However, even with access to the DOM and a full suite of playwright tools, the LLM can still at times miss the mark. There is great utility in being able to record your own interactions to smooth over the few steps of the process that are still too daunting for the LLM to navigate on its own. 

Workflow and automation MCP projects

3. n8n MCP Server by Leonard Sellem (185 ⭐ on Github)

The n8n MCP Server bridges the gap between conversational AI and n8n's powerful workflow automation platform. This integration allows your LLM to manage complex workflows and execute automations through natural language.

External technologies:

• n8n REST API

• Axios

• Node.js

• TypeScript

• JSON Schema

API keys required:

• N8N_API_URL: URL of the n8n API

• N8N_API_KEY: API key for authenticating with n8n

• Optional: N8N_WEBHOOK_USERNAME and N8N_WEBHOOK_PASSWORD for webhook authentication

This integration allows your LLM to become a hands-on automation partner; you can manage complex workflows through conversation. The n8n platform itself supports over 400+ integrations with popular services and tools, making this MCP server a powerful gateway to a vast ecosystem of automation possibilities.

Imagine describing a workflow to your LLM in natural language and having it translate your requirements into a fully functional n8n workflow, or troubleshooting a failed execution by simply asking your LLM what went wrong.

4. MCP LangGraph Tools by Paul Robello (38 ⭐ on Github)

MCP LangGraph Tools provides a bridge between MCP tools and LangGraph, enabling complex AI-powered workflows that combine the strengths of different frameworks. This one is not an MCP server itself, but rather provides a framework for integrating existing MCP tools that servers provide into LangGraph. 

External technologies:

• LangGraph

• LangChain

• Multiple LLM providers (Anthropic, OpenAI, Google, Groq, Ollama)

• Brave Search API (in example implementation)

API keys required:

• Brave Search API Key (for example implementation)

• LLM provider API key (Anthropic, OpenAI, Google, Groq, or Ollama setup)

This integration is particularly valuable for developers building complex AI workflows that need to orchestrate multiple tools and maintain state. By combining MCP's standardized tool access with LangGraph's orchestration capabilities, developers can create sophisticated AI applications with less custom integration code. Leveraging LangChain is also more code-friendly than an n8n-native approach.

The implementation supports multiple LLM providers, making it a flexible solution for teams working with different AI models. The repository's example implementation demonstrates how easily an AI assistant can access web search capabilities through MCP while maintaining LangGraph's powerful state management. 

Human-AI Collaboration MCP Servers

5. gotoHuman MCP Server by gotohuman (16 ⭐ on Github)

The gotoHuman MCP Server bridges AI agents and human reviewers, enabling AI applications to request human reviews, approvals, and inputs in an asynchronous workflow. Access to human feedback is handled by goToHuman themselves – you are not the human providing the input. If you are looking for a server that only requests feedback from you, on your local machine, check out this honorable mention: interactive-mcp.

External technologies:

• TypeScript

• Node.js

• Zod (for schema validation)

• gotoHuman API

API keys required:

• GOTOHUMAN_API_KEY: Authentication key for the gotoHuman service

This server addresses the critical need for human oversight in AI agent operations. It creates a structured workflow for AI systems to request human judgment on essential decisions or content, which is potentially valuable for applications in regulated industries or situations where human approval is necessary. At the same time, I’m unsure what guarantees you can have that humans will be experts in the applicable domain (if any). 

Nevertheless, the webhook-based asynchronous workflow means AI agents can continue their operations while waiting for human input, creating a more efficient system for human-in-the-loop AI applications. This approach might be ideal for solopreneurs and small companies that don’t want to have someone on staff dedicated to the work. 

6. Record to Markdown by Mike Li (0 ⭐ on Github)

Record to Markdown provides a simple but useful bridge between your LLM conversations and local note-taking applications, allowing users to save conversations directly to Markdown files or Apple Notes. Because the output is can be saved in markdown, it might also work well with tools like obsidian.md.

External technologies:

• FastMCP

• httpx

• markdown2

• AppleScript (osascript) for Apple Notes integration

API keys required: None

While conceptually simple, this tool addresses a common need among LLM users: preserving valuable conversation content for later reference. Completing one task might at times take multiple chats, and carrying information over between chats can be tedious, especially as a chat approaches its context limit.

By enabling direct saving to local note-taking systems, Record to Markdown streamlines the knowledge capture process and reduces the friction of transferring insights from AI conversations to personal knowledge bases. The Apple Notes integration is great for people in the Apple Ecosystem, using AppleScript to create properly formatted notes with tags for easy organization. 

Infrastructure and integration MCP servers

7. MCP Installer by Ani Betts (1k ⭐ on Github)

MCP Installer solves a critical "chicken and egg" problem in the MCP ecosystem by providing a streamlined way to install and configure additional MCP servers on demand. I find myself wondering if this is going to evolve into fulfilling a role like ComfyUI-Manager does in the local text-to-image ecosystem. 

External technologies:

• spawn-rx

• rimraf

• npx

• uv (Python package installer)

API keys required: None for the installer itself, though individual MCP servers may require their own API keys

MCP Installer addresses a significant barrier to entry in the MCP ecosystem - the initial setup complexity. Creating a dedicated MCP server that handles the installation of other servers makes the discovery and installation of MCP servers conversational and accessible to users without command-line expertise.

The community recognizes this tool as a foundation that enables the adoption of other specialized MCP servers. It serves as a multiplier in the ecosystem, creating a virtuous cycle whereby we get to enjoy even more tool integrations. 

8. MCPAdapt by Guillaume Raille (301 ⭐ on Github)

MCPAdapt bridges the gap between MCP servers and popular agentic frameworks like LangChain, CrewAI, and Google GenAI, allowing tools from the MCP ecosystem to be used within these frameworks. Like MCP LangGraph Tools, this is not a server itself. Rather, it acts as an adapter, enabling the use of MCP tools within different agentic frameworks. 

External technologies:

• jsonref

• pydantic

• python-dotenv

• Optional dependencies for specific frameworks (crewai, langchain, google-genai, etc.)

API keys required: None for MCPAdapt itself, though the agentic frameworks and MCP servers it connects may require their own API keys

MCPAdapt represents a powerful integration layer, enabling developers to leverage the rapidly expanding ecosystem of MCP tools within their preferred AI agent development environment. This cross-framework compatibility accelerates the adoption of both MCP tools and agentic frameworks by eliminating the need to choose between ecosystems.

The library supports both synchronous and asynchronous usage patterns, with clever threading to avoid blocking in synchronous mode. This flexibility makes it adaptable to various application architectures and development patterns.

9. Multiverse MCP Server by Enrico Ballardini (47 ⭐ on Github)

The Multiverse MCP Server allows users to run multiple instances of the same MCP server, each with its own distinct configuration and environment, preventing naming collisions and enabling more complex setups. While this one technically is an MCP server, the tools it provides depend entirely on how you configure it alongside your other servers. It can wrap other MCP servers, prefixing their tool names to denote that they are instanced, thereby enabling isolated instances of the same server with unique configurations.

External technologies:

• Node.js

• @modelcontextprotocol/sdk

• Commander.js

• Zod

• Chokidar

API keys required: None for Multiverse itself, though the wrapped MCP servers may require their own API keys

Multiverse solves the practical problem of running multiple instances of the same server type with distinct configurations. For example, you could have two filesystem MCP servers, each with access to different directories, or multiple GitHub MCP servers with different personal access tokens.

The server's clever prefixing system ensures that tool names remain organized, preventing naming collisions. The extensive configuration options allow for fine-grained control over each wrapped server, including the ability to hide specific functions or apply path resolution to create sandboxed environments.

Comparative analysis

MCP ecosystem integration support

If a compatibility is crossed out, it may still work – it was just not an explicitly mentioned compatibility in the repo. 

Using these MCP servers together

The true power of these MCP servers emerges when they're used in combination, creating workflows that leverage each tool's strengths. A few compelling scenarios come to mind:

AI-driven web app development

Scenario: A developer working on a web application with automated testing.

1. Use MCP Installer to set up both Claude Debugs for You and Playwright MCP

2. With Playwright MCP, Claude can inspect the web application's DOM and capture screenshots of UI issues

3. Using Claude Debugs for You, Claude can help debug the underlying code causing those issues

4. The findings can be saved to a knowledge base using Record to Markdown for team sharing

This combination creates a powerful development assistant that can both identify visual issues and help resolve the underlying code problems, all while documenting the process for future reference.

AI-human collaborative workflow

Scenario: An AI agent managing a content production workflow with human oversight.

1. Use n8n MCP Server to orchestrate a content production workflow that includes data gathering, content generation, and publishing steps

2. Integrate gotoHuman MCP Server to request human approval at critical stages

3. Use Record to Markdown to archive approved content and feedback for training purposes

4. Leverage MCPAdapt to connect these MCP tools with a LangChain agent that manages the overall process

This integration creates a robust content production system with appropriate human oversight, combining automation efficiency with human judgment where it matters most.

Multi-environment development (with isolation)

Scenario: A developer working across multiple projects with different requirements.

1. Use MCP Installer to install base MCP servers like filesystem access and GitHub integration

2. Configure Multiverse MCP Server to create isolated instances for each project, preventing cross-project access

3. Use Claude Debugs for you, configured through Multiverse to provide debugging assistance across multiple codebases

4. Integrate with MCP LangGraph Tools to create project-specific AI workflows tailored to each environment

This setup allows a developer to maintain strict separation between projects while still leveraging AI assistance across all of them, with appropriate access controls and customized workflows for each context.

Why these MCP servers matter for power users

The MCP ecosystem represents a fundamental shift in how we interact with AI assistants, moving from conversation-only interactions to truly capable AI collaborators that can affect the digital world. For power users, these tools offer several key benefits:

1. Extended Capabilities: MCP servers dramatically expand what LLMs can do, enabling them to interact with development environments, automation platforms, browsers, and more.

2. Workflow Integration: Rather than using your LLM with lots of copying and pasting, these tools allow for integration with existing workflows and tools, making AI assistance contextual and relevant.

3. Reduced Context Window Usage: By accessing external tools and data sources directly, an LLM can work with much larger datasets and codebases without consuming valuable context window space. (Though one must be careful about how much context space is used up by providing the LLM with tool definitions.)

4. Specialized Expertise: Each MCP server effectively gives the LLM domain-specific capabilities in areas like debugging, web automation, or workflow management itself.

5. Ecosystem Growth: As the MCP ecosystem continues to expand, power users who adopt these tools early will have an increasing advantage as more capabilities become available.

The future of AI collaboration

The MCP servers we've explored represent the beginning of a new paradigm in AI assistance – one where AI tools aren't limited to conversation but can actively participate in complex workflows, access specialized tools, and bridge the gap between thought and action.

The rewards are substantial for power users willing to invest in setting up these tools: a dramatically more capable AI assistant that can help with debugging, automation, web interaction, and much more. As the MCP ecosystem grows, we can expect even more innovative tools to emerge, further expanding what's possible at the intersection of human creativity and AI capability.

Whether you're a developer looking for a debugging partner, an automation enthusiast wanting conversational control of your workflows, or simply someone who wants to get more value from AI assistants, these MCP servers offer a glimpse into the future of AI collaboration – a future where the boundary between human and machine capabilities becomes increasingly fluid and productive.

Secure your AI-generated apps with Snyk

These AI tools are exciting for automation, but they can still make mistakes, especially when it comes to generating secure code. Snyk can scan your code for vulnerabilities, right in your IDE. 

If you want enterprise access to Snyk’s top-of-the-line tools – an experience without the same rate limits as the free tier – you can apply to gain enterprise access for your open-source project free of cost. This offering comes from our Secure Developer project. Check out some of the projects that have already joined us, too!