MCP Compass
This server is a discovery and recommendation tool for finding external Model Context Protocol (MCP) servers based on natural language queries.
Find MCP Servers: Searches and recommends existing MCP servers from the internet based on specific needs.
Detailed Results: Returns information including server IDs, descriptions, GitHub URLs, and similarity scores.
Specific Queries: Works best with specific and actionable requests to ensure accurate recommendations.
Real-time Information: Provides up-to-date recommendations from currently available MCP servers.
The README includes a demo image showing Airtable Server Search as an example of MCP Compass capabilities, suggesting it can help discover and connect to Airtable MCP services.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Compassfind MCP servers for data visualization"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Compass 🧭
MCP Discovery & Recommendation
Experience MCP Compass 🌐
You can now experience MCP discovery directly on our website!
👉 Explore MCP Compass 👈
Dive in to discover the power of MCP services in action with real-time recommendations and insights.
Related MCP server: MCPfinder Server
What is this? 🤔
MCP Compass is a discovery & recommendation service that helps you explore Model Context Protocol servers. It acts as a smart guide that helps AI assistants find and understand available MCP services out there based on natural language queries, making it easier to discover and utilize the right tools for specific tasks.
Quick Example
Features 🌟
🔍 Smart Search: Find MCP services using natural language queries
📚 Rich Metadata: Get detailed information about each service
🔄 Real-time Updates: Always up-to-date with the latest MCP services
🤝 Easy Integration: Simple to integrate with any MCP-compatible AI assistant
Quick Start 🚀
Usage
Clone the repository
or
Use
npx
Installation
For Claude Desktop, edit your claude_desktop_config.json file:
MacOS/Linux
code ~/Library/Application\ Support/Claude/claude_desktop_config.jsonWindows
code $env:AppData\Claude\claude_desktop_config.jsonAs an MCP Service:
Add to your AI assistant's MCP configuration to enable service discovery capabilities.
{ "mcpServers": { "mcp-compass": { "command": "npx", "args": [ "-y", "@liuyoshio/mcp-compass" ] } } }or
{ "mcpServers": { "mcp-compass": { "command": "node", "args": [ "/path/to/repo/build/index.js" ] } } }
License 📝
MIT License - See LICENSE file for details.
Support 💬
Available Tools
1 toolrecommend-mcp-serversB
Use this tool when there is a need to findn external MCP tools. It explores and recommends existing MCP servers from the internet, based on the description of the MCP Server needed. It returns a list of MCP servers with their IDs, descriptions, GitHub URLs, and similarity scores.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Description for the MCP Server needed. It should be specific and actionable, e.g.: GOOD: - 'MCP Server for AWS Lambda Python3.9 deployment' - 'MCP Server for United Airlines booking API' - 'MCP Server for Stripe refund webhook handling' BAD: - 'MCP Server for cloud' (too vague) - 'MCP Server for booking' (which booking system?) - 'MCP Server for payment' (which payment provider?) Query should explicitly specify: 1. Target platform/vendor (e.g. AWS, Stripe, MongoDB) 2. Exact operation/service (e.g. Lambda deployment, webhook handling) 3. Additional context if applicable (e.g. Python, refund events) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool explores the internet and returns a list with IDs, descriptions, GitHub URLs, and similarity scores, which covers some behavioral aspects. However, it lacks details on rate limits, authentication needs, potential errors, or how the exploration works (e.g., API calls, web scraping). For a tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the usage context and then detailing the action and return values. It uses three sentences efficiently, with no wasted words, though it could be slightly more polished (e.g., 'findn' typo).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It explains the purpose, usage, and return format, but lacks behavioral details like error handling or exploration mechanics. Without annotations or an output schema, more context would be beneficial for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, providing detailed examples and requirements for the 'query' parameter. The description adds minimal value beyond this, only mentioning that it's 'based on the description of the MCP Server needed.' Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'explores and recommends existing MCP servers from the internet, based on the description of the MCP Server needed.' It specifies the verb (explores/recommends) and resource (MCP servers), though it doesn't need to differentiate from siblings since none exist. The purpose is specific but could be slightly more precise about the exploration mechanism.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: 'when there is a need to find external MCP tools.' It explicitly ties usage to the query parameter's description of the needed MCP server. However, it doesn't mention when not to use it or alternatives, which isn't critical here since no siblings exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
recommend-mcp-servers
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as recommending MCP servers based on descriptions, and no other tools exist to cause confusion.
A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The tool name 'recommend-mcp-servers' follows a clear verb-noun pattern and uses hyphens consistently.
One tool is too few for a server named 'MCP Compass', which suggests a broader scope of functionality for exploring or navigating MCP servers. A single recommendation tool feels thin and incomplete for this apparent purpose, lacking supporting tools like search, filter, or get details.
The tool surface is severely incomplete for the inferred domain of MCP server exploration. While the tool can recommend servers, there are significant gaps: no ability to search, filter, get detailed information, list categories, or manage preferences. This will likely cause agent failures when trying to perform comprehensive MCP server discovery tasks.
Maintenance
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