MCP Connector

Discover and Configure MCP Servers Automatically

1MCP Server is an MCP-of-MCPs connector that auto-discovers, tests, and configures other MCP servers from one natural-language request.

Works with github

84
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Updated 8 months ago
Source checked Sep 15, 2026
Version 1.0.0
Models
universal

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Why it matters

Eliminate manual MCP server setup. This asset automatically discovers, selects, and configures necessary MCP servers for your tasks, streamlining your development and testing processes.

Outcomes

What it gets done

01

Automatically discover and configure MCP servers.

02

Perform quick and deep searches for specific MCP server needs.

03

Generate test code to verify MCP server functionality.

04

Build multi-step workflows using discovered MCP servers.

Source

Get it from source

Spark does not host a copy of it.

Open source

Reports

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Capabilities

Tools your agent gets

quick_search

Returns a list of MCP servers for specific purposes

deep_search

Breaks down complex tasks into components and finds suitable MCP servers

test_server_template_code

Returns sample test code for verifying MCP server functionality

Overview

1mcpserver MCP Server

1MCP Server is a single MCP connector that automatically discovers, tests, and configures other MCP servers for an AI client. It exposes a deep_search tool that breaks a high-level goal into components, finds a matching MCP server for each part, and asks whether to skip, decompose further, or implement missing pieces directly. It runs as a hosted remote endpoint or locally via Docker, npx, or from source. Use it when you want an MCP client to automatically find and configure whatever MCP servers a task requires, instead of installing each one by hand. Skip it if you already know the one or two MCP servers you need and can configure them directly.

What it does

1MCP Server acts as an "MCP of MCPs": a single MCP connector that automatically discovers, selects, and configures other MCP servers on a user's machine, whether those servers run locally or remotely. Once it is set up, a user or an AI agent can describe what they want in natural language and call its deep_search tool, and 1MCP Server handles searching for matching MCP servers, selecting them, and configuring their entries. It exposes two search modes: Quick Search, for explicit requests like finding an MCP server for payments, which returns a shortlist of relevant servers; and Deep Search, for higher-level goals, where the underlying LLM breaks the goal into components, looks for an MCP server for each part, and asks whether to ignore that part, break it down further, or implement it directly when nothing matches. Deep Search runs in three stages: Planning, which identifies the servers, API keys, and config changes needed; Testing, which verifies each candidate server via the test_server_template_code tool; and Acting, which executes the workflow using the servers that were configured. Its server catalog draws on data from wong2/awesome-mcp-servers, metorial/mcp-containers, punkpeye/awesome-mcp-servers, and modelcontextprotocol/servers, and the project is also published to mcpservers.org and glama.ai/mcp/servers.

When to use - and when NOT to

Reach for 1MCP Server when a client or agent needs to work across many potential MCP servers and you would rather not research, install, and configure each one individually - it lets you install this one connector and let it locate and wire up the rest as tasks come up. It is less useful when you already know exactly which one or two MCP servers you need, since configuring them directly is simpler than routing through a discovery layer. The project's own future-work notes flag it as still evolving: planned items include a dedicated server-to-server messaging protocol (MCCP) and improvements to its MCP server database schema, so treat it as an actively developing discovery layer rather than a finished catalog.

Capabilities

  • Runs over streamable-http for the hosted remote endpoint, or over stdio for local Docker, npx, uvx, and from-source setups, so it fits clients that require either transport
  • The same connector config works across Cursor, Gemini CLI, Claude Desktop, and Codex

How to install

The simplest path is the hosted remote endpoint at https://mcp.1mcpserver.com/mcp/, added to an MCP client's config (for example Claude Desktop's claude_desktop_config.json) under mcpServers. For a fully local setup, or if your client only supports STDIO, it can also run via Docker:

docker run -p 8080:8080 ghcr.io/particlefuture/1mcpserver:latest

It is also available via npx, or by cloning the source repository and running it directly with uv run server.py --local.

Who it's for

Developers and teams who want one MCP connector to discover and configure whatever other MCP servers a given task needs, instead of manually researching and installing each MCP server up front.

Source README

1 MCP Server 🚀

Website

MCP of MCPs - automatically discover and configure MCP servers on your machine (remote or local).

After setup, you can usually just say:

“I want to perform . Call the deep_search tool and follow the outlined steps.”

The goal is that you only install this MCP server, and it handles the rest (searching servers, selecting servers, configuring servers, etc.).

Demo video 🎥: https://youtu.be/W4EAmaTTb2A

Quick Setup

Choose one of the following:

  1. Remote (simplest & fastest ⚡💨)
  2. Local (prebuilt) - Docker, uvx, or npx
  3. Local (from source) - run this repo directly

1) Remote 🌍⚡💨

Use the hosted endpoint (recommended for the simplest setup).

Docs + guided setup: https://mcp.1mcpserver.com/

Configure your MCP client

Add the following entry to your client config file:

  • Cursor: ./.cursor/mcp.json
  • Gemini CLI: ./gemini/settings.json (see Gemini docs)
  • Claude Desktop:
    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • Codex:
    • macOS: ~/.codex/config.toml
    • Windows: %USERPROFILE%\.codex\config.toml

Remote config (JSON):

{
  "mcpServers": {
    "1mcpserver": {
      "url": "https://mcp.1mcpserver.com/mcp/",
      "headers": {
        "Accept": "text/event-stream",
        "Cache-Control": "no-cache"
      }
    }
  }
}

If you already have other servers configured, just merge this entry under mcpServers For example:

{
  "mcpServers": {
    "1mcpserver": {
      "url": "https://mcp.1mcpserver.com/mcp/",
      "headers": {
        "Accept": "text/event-stream",
        "Cache-Control": "no-cache"
      }
    },
    "file-system": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "."]
    }
  }
}

Tip: If your client supports it, move the config file into your home directory to apply globally.


2) Local (prebuilt) 💻

Use this when you want everything local, or when your MCP client only supports STDIO.

2A) Docker 🐳
 docker run -p 8080:8080 ghcr.io/particlefuture/1mcpserver:latest

Running on other host ports:

 docker run -p <FREE_HOST_PORT_NUM>:8080 ghcr.io/particlefuture/1mcpserver:latest

Running with stdio instead of streamable-http (You might see some delays when trying to connect):

run --rm -i ghcr.io/particlefuture/1mcpserver:latest --local
{
  "mcpServers": {
    "1mcpserver": {
      "url": "https://mcp.1mcpserver.com/mcp/"
    }
  }
}
2B) npx 📦
npx -y @1mcpserver/1mcpserver

3) Local (from source) 🧩

Clone this repo and run directly.

git clone https://github.com/particlefuture/MCPDiscovery.git
cd MCPDiscovery
uv sync
uv run server.py --local
{
  "mcpServers": {
    "1mcpserver": {
      "command": "/path/to/uv",
      "args": [
        "--directory",
        "<PATH_TO_CLONED_REPO>",
        "run",
        "server.py",
        "--local"
      ]
    }
  }
}

If your client supports remote url servers, you can use the Remote setup instead.

Optional: grant file-system access 📁

If you want your LLM to have file-system access, add an MCP filesystem server and point it at the directory you want to allow:

{
  "mcpServers": {
    "file-system": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "~/"]
    }
  }
}

Architecture 🧠

There are two search modes:

Quick Search ⚡

For explicit requests like: “I want an MCP server that handles payments.”

Returns a shortlist of relevant MCP servers.

Deep Search 🌊

For higher-level or complex goals like: “Build a website that analyzes other websites.”

The LLM breaks the goal into components/steps, finds MCP servers for each part, and if something is missing, it asks whether to:

  • ignore that part,
  • break it down further, or
  • implement it ourselves.

Deep Search stages:

  1. Planning - identify servers, keys, and config changes
  2. Testing - verify servers (via test_server_template_code)
  3. Acting - execute the workflow using the configured servers

Change Log 🕒

  • July 31 2025: Upgrade to 0.2.0. Added agentic planning.
  • Dec 12 2025: Support for Gemini + Codex
  • Dec 13 2025: Easier local setup with docker and npm.

 

Future 🔮

  • Better demo videos (new domain, narrated walkthrough)
  • Model Context Communication Protocol (MCCP): standard server-to-server messaging
  • Avoid calling tools with an internal_ prefix unless instructed
  • Improve MCP server database schema: server, description, url, config json, extra setup (docker/api key/etc)

Troubleshooting 🧰

  • If using a venv and you get ModuleNotFoundError even after installing: delete the venv and recreate it.

Please create an issue or directly contact me zjia71@gatech.edu if you encounter ANY issue of frustration. I really hope the setup is as smooth as possible!!

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