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mcp-server-collector MCP server

mcp-server-collector MCP server

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github.com/chatmcpDeveloper Tools20Updated 98w ago
Repository

About mcp-server-collector MCP server

mcp-server-collector MCP server is an MCP server for collecting MCP servers from the internet. It extracts server entries from URLs or text content and submits them to a directory, aimed at directory maintainers and developers.

Key features

  1. 01Extracts MCP servers from a given URL
  2. 02Extracts MCP servers from supplied text content
  3. 03Submits MCP server entries to a directory
  4. 04Configured through a .env file for OpenAI-compatible settings

Use cases

  1. 01Collecting MCP server lists from web pages
  2. 02Submitting newly found MCP servers to a directory site

MCP support

This is an MCP server that exposes extract-mcp-servers-from-url, extract-mcp-servers-from-content and submit-mcp-server tools to MCP clients.

#mcp-server-collector#submit-mcp-server

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

mcp-server-collector MCP server

A MCP Server used to collect MCP Servers over the internet.

Components

Resources

No resources yet.

Prompts

No prompts yet.

Tools

The server implements 3 tools:

  • extract-mcp-servers-from-url: Extracts MCP Servers from given URL.
    • Takes "url" as required string argument
  • extract-mcp-servers-from-content: Extracts MCP Servers from given content.
    • Takes "content" as required string argument
  • submit-mcp-server: Submits a MCP Server to the MCP Server Directory like mcp.so.
    • Takes "url" as required string argument and "avatar_url" as optional string argument

Configuration

.env file is required to be set up.

OPENAI_API_KEY="sk-xxx"
OPENAI_BASE_URL="https://api.openai.com/v1"
OPENAI_MODEL="gpt-4o-mini"

MCP_SERVER_SUBMIT_URL="https://mcp.so/api/submit-project"

Quickstart

Install
Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development/Unpublished Servers Configuration ``` "mcpServers": { "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] }, "mcp-server-collector": { "command": "uv", "args": [ "--directory", "path-to/mcp-server-collector", "run", "mcp-server-collector" ], "env": { "OPENAI_API_KEY": "sk-xxx", "OPENAI_BASE_URL": "https://api.openai.com/v1", "OPENAI_MODEL": "gpt-4o-mini", "MCP_SERVER_SUBMIT_URL": "https://mcp.so/api/submit-project" } } } ```
Published Servers Configuration ``` "mcpServers": { "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] }, "mcp-server-collector": { "command": "uvx", "args": [ "mcp-server-collector" ], "env": { "OPENAI_API_KEY": "sk-xxx", "OPENAI_BASE_URL": "https://api.openai.com/v1", "OPENAI_MODEL": "gpt-4o-mini", "MCP_SERVER_SUBMIT_URL": "https://mcp.so/api/submit-project" } } } ```

Development

Building and Publishing

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:
uv sync
  1. Build package distributions:
uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:
uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN
  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD
Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory path-to/mcp-server-collector run mcp-server-collector

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Community

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.