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MCPDiscovery

by particlefuture

Discovers, evaluates, and configures local or remote MCP servers through quick and deep search workflows.

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Install MCPDiscovery

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "particlefuture-mcpdiscovery": {
      "command": "npx",
      "args": [
        "-y",
        "@1mcpserver/1mcpserver"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y @1mcpserver/1mcpserver

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 24d ago.

Overview

particlefuture/MCPDiscovery MCP server helps an agent find and configure other MCP servers on a local machine or through a hosted endpoint. It supports quick searches for direct needs and deep searches that plan multi-step goals, test candidate servers, and execute configured workflows. Use it when you want one MCP entry point to locate servers instead of configuring each server manually. Deployment options include a remote streamable-HTTP endpoint, Docker, npx, uvx, or running the source locally.

Use cases

  • Find an MCP server for a specific capability
  • Plan a multi-step workflow across MCP servers
  • Test candidate server templates before use
  • Configure MCP servers from one client entry point

Key features

  • Quick search for explicit requirements
  • Deep search with planning, testing, and acting stages
  • Local and remote server discovery
  • Remote streamable-HTTP endpoint
  • Docker, npx, uvx, and source-based deployment
  • Candidate configuration and key planning

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

19/20 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 10d ago via OSV.dev · @1mcpserver/1mcpserver (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars51Stargazers on the source repository.
npm downloads59Package downloads in the last 30 days.
Last commit10mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How MCPDiscovery works

What particlefuture/MCPDiscovery MCP server does

particlefuture/MCPDiscovery MCP server acts as an MCP entry point for discovering other MCP servers. It can search a catalog of servers, identify candidates for a requested capability, and help prepare the configuration needed to use them. The catalog draws from several public MCP server collections, including awesome-mcp-servers repositories, Metorial MCP containers, and the official Model Context Protocol server repository.

The project supports two discovery styles. Quick Search is intended for a clearly stated requirement, such as finding an MCP server for payments. Deep Search handles broader goals by dividing them into components and looking for servers that cover each part. When no suitable server is found for a component, the workflow can ask whether to skip it, decompose it further, or implement it directly.

How it works

A deep-search workflow has three stages. Planning identifies candidate servers, required keys, and configuration changes. Testing verifies server templates through test_server_template_code. Acting then runs the workflow with the servers that have been configured. The README also presents deep_search as a tool an agent can call after describing the desired outcome.

The server can be used remotely at the hosted MCP endpoint or run locally. Remote use lets an MCP client connect with a URL and HTTP headers. Local deployments can expose streamable HTTP through Docker or use standard input/output, including the source-based --local mode. The local approach is useful when the client only supports STDIO or when keeping the service on the local machine is preferred.

Setup and configuration

The simplest option is to add the hosted URL, https://mcp.1mcpserver.com/mcp/, to an MCP client configuration. The documented examples cover Claude Desktop, Cursor, Gemini CLI, and Codex. The remote entry includes Accept: text/event-stream and Cache-Control: no-cache headers.

For local use, Docker runs the published image on port 8080, while npx launches @1mcpserver/1mcpserver. A source installation clones the repository, runs uv sync, and starts uv run server.py --local. The client can then invoke the process with the configured path to uv and repository directory.

If discovered workflows need filesystem access, configure a separate MCP filesystem server and restrict it to the directory the agent should access.

Tools and capabilities

  • Find MCP servers for explicit capabilities.
  • Break larger goals into searchable components.
  • Identify server keys and configuration changes during planning.
  • Test server template code before use.
  • Execute workflows through configured servers.
  • Run through remote HTTP, Docker, npx, uvx, or local source deployment.

Limitations and notes

Discovery and execution depend on the candidate servers selected by the workflow and any credentials or extra setup those servers require. STDIO operation through Docker may introduce connection delays. If a virtual environment reports an import error after installation, the troubleshooting guidance recommends recreating the environment.

The project lists server-to-server messaging, improved database fields, and other changes as future work rather than current capabilities. particlefuture/MCPDiscovery MCP server therefore fits best when an agent needs a discovery and configuration layer; it should not be treated as a replacement for every underlying MCP server it finds.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about MCPDiscovery

particlefuture/MCPDiscovery is an MCP server that connects an agent to a catalog of local and remote MCP servers. Its main tools and workflows support quick discovery, deep_search planning, server-template testing through test_server_template_code, configuration planning, and execution across the selected servers.

Add the hosted MCP URL to your client, or run it locally with Docker, npx, uvx, or from source. The source path uses `uv sync` followed by `uv run server.py --local`; the README also provides client configuration examples.

Yes, Cursor is one of the documented clients. Add the remote server entry to `./.cursor/mcp.json`, or configure the local command according to the Docker, npx, or source-based setup you choose.

Yes, local STDIO operation is supported.

Deep search breaks a broad goal into components, identifies servers and configuration needs, tests candidate server templates, and then acts through the configured servers. If a component lacks a suitable server, the workflow can ask to ignore it, decompose it further, or implement it.

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