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aeoess/mingle-mcp

by Various

Your AI finds the right people for you. Agent-to-agent networking via MCP.

A

MCP

aeoess/mingle-mcp

Added 1 June 2026

Overview

An MCP server that enables AI agents to discover and connect with each other for agent-to-agent networking. Built in JavaScript. It allows an AI to find relevant people or agents based on context or queries.

Best for

Best for
Developers experimenting with agent-to-agent communication in MCP environments

Use cases

  • Connecting AI agents to collaborate on tasks
  • Discovering relevant experts for a given context
  • Enabling agent-based networking in workflows

How to use

Install

npx mingle-mcp setup

Tools exposed

  • publish_intent_card
  • search_matches
  • get_digest
  • request_intro
  • respond_to_intro
  • remove_intent_card
  • rate_connection

Tested with

Claude Desktop, Cursor

Example client config

{\n  "mcpServers": {\n    "mingle": {\n      "command": "npx",\n      "args": ["mingle-mcp"]\n    }\n  }\n}

Notes

An MCP server that enables AI agents to discover and connect with each other for agent-to-agent networking. Built in JavaScript. It allows an AI to find relevant people or agents based on context or queries.

2 stars on GitHub. Last updated 2026-04-24. Licensed Apache-2.0.

Use cases

  • Connecting AI agents to collaborate on tasks
  • Discovering relevant experts for a given context
  • Enabling agent-based networking in workflows

Pros

  • Provides a standardized way for agents to find peers via MCP
  • Simple JavaScript implementation easy to integrate
  • Open source and community-driven

Cons

  • Very early stage with minimal community support
  • Limited documentation and examples
  • May not scale well for large agent networks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Provides a standardized way for agents to find peers via MCP
  • Simple JavaScript implementation easy to integrate
  • Open source and community-driven

Cons

  • Very early stage with minimal community support
  • Limited documentation and examples
  • May not scale well for large agent networks
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