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mcp-server-collector

mcp-server-collector

by chatmcp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

mcp-server-collector MCP server

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

Components

Resources

No resources yet.

Prompts

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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

Related MCP server: MCP Server Neurolorap

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

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

About the author

Available Tools

3 tools
extract-mcp-servers-from-contentC

Extract MCP Servers from given content

Inputs: content (required).

extract-mcp-servers-from-urlC

Extract MCP Servers from a URL

Inputs: url (required).

submit-mcp-serverC

Submit MCP Server to MCP Servers Directory like mcp.so

Inputs: url (required), avatar_url.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.4/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: extracting servers from content, extracting from a URL, and submitting to a directory. The descriptions make it unambiguous which tool to use for each scenario, preventing misselection.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with hyphens (e.g., extract-mcp-servers-from-content, extract-mcp-servers-from-url, submit-mcp-server). This predictable naming scheme enhances readability and usability for agents.

    Tool Count5/5

    With 3 tools, the server is well-scoped for its purpose of collecting and submitting MCP servers. Each tool earns its place by covering distinct aspects of the workflow, avoiding bloat or thinness.

    Completeness4/5

    The tool set covers the core workflow of extraction (from content and URLs) and submission, with no obvious dead ends. A minor gap might be the lack of tools for managing or listing already submitted servers, but agents can work around this with the existing tools.

    Maintenance

    ActivityInactive
    ResponsivenessLimited data

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