MCP Talk

MCP Talk

A lightweight messaging system that enables real-time communication between different AI agents through a shared, file-based message queue. It provides tools for sending, checking, and broadcasting messages to facilitate coordination across isolated project namespaces.

Category
Visit Server

README

MCP Talk

Inter-agent messaging via Model Context Protocol (MCP).

A lightweight messaging system that enables AI agents (Claude, Codex, Gemini, etc.) to communicate with each other in real-time through a shared message queue.

Features

  • Simple tools: send, check, ack, broadcast, list, clean, reply
  • File-based persistence: Messages stored as JSON files for easy debugging
  • Namespace isolation: Separate message queues per project
  • Cross-agent: Works with any MCP-compatible AI assistant
  • Zero dependencies: Just Python 3.10+ and the MCP SDK

Installation

# From PyPI (recommended)
pipx install mcp-talk

# Or with pip
pip install mcp-talk

# From source
git clone https://github.com/devinvenable/mcp-talk.git
cd mcp-talk
pipx install .

Configuration

Add to your MCP client configuration:

Claude Desktop / Claude Code

{
  "mcpServers": {
    "mcp-talk": {
      "command": "mcp-talk"
    }
  }
}

Using uvx (no install required)

{
  "mcpServers": {
    "mcp-talk": {
      "command": "uvx",
      "args": ["mcp-talk"]
    }
  }
}

Codex CLI (~/.codex/config.toml)

[mcp_servers.mcp-talk]
command = "mcp-talk"

Gemini CLI (~/.gemini/settings.json)

{
  "mcpServers": {
    "mcp-talk": {
      "command": "mcp-talk"
    }
  }
}

Tools

send - Send a direct message

send(to="codex", message="Please review PR #123", from_agent="claude")

check / chk - Check messages

check(agent="claude")
chk(agent="claude", include_body=true, auto_ack=true)

Returns up to 5 messages by default. Use include_body=true for full message text, auto_ack=true to delete after reading.

broadcast - Send to all agents

broadcast(message="Standup in 5 minutes", from_agent="pm")

ack - Acknowledge/delete a message

ack(id="20251126_143022_abc12345")

reply - Reply to a message

reply(id="20251126_143022_abc12345", message="Done!", from_agent="gemini")

Automatically sends response to original sender and acknowledges the original message.

list - List all messages (PM view)

list(limit=10, include_body=true)

clean - Remove old messages

clean(hours=24)

Namespaces

Isolate messages between projects using the namespace parameter:

# Game project
send(to="gemini", message="Review level 3", namespace="game")
check(agent="gemini", namespace="game")

# Work project
send(to="gemini", message="Review PR #123", namespace="work")
check(agent="gemini", namespace="work")

Messages are stored in separate directories:

~/.mcp_talk/q/           # default (no namespace)
~/.mcp_talk/q/game/      # namespace="game"
~/.mcp_talk/q/work/      # namespace="work"

Message Format

Messages are stored as JSON files in ~/.mcp_talk/q/:

{
  "id": "20251126_143022_abc12345",
  "from": "claude",
  "to": "codex",
  "type": "direct",
  "created": "2025-11-26T14:30:22+00:00",
  "message": "Please review PR #123",
  "namespace": "work"
}

Environment Variables

Variable Default Description
MCP_TALK_QUEUE ~/.mcp_talk/q/ Message queue directory
MCP_TALK_AUTO_CLEAN_HOURS 24 Auto-delete messages older than N hours (0 to disable)
MCP_TALK_MAX_MESSAGE_CHARS 2000 Maximum message length

Multi-Agent Setup Tips

Teaching agents to check messages

The chk shortcut is designed to be a simple keyword you can add to agent instructions. Add to your agent's system prompt or memory:

Gemini (~/.gemini/instructions.md):

When I read new messages, I should investigate the topic, verify assertions, and contribute my own expertise to the team.

Claude (CLAUDE.md in project):

When starting work, check for messages with: chk(agent="claude")

Codex (~/.codex/instructions.md):

Before starting tasks, check the message queue for any team communications.

Recommended MCP config with env overrides

Customize behavior per-agent with environment variables:

# ~/.codex/config.toml
[mcp_servers.mcp-talk]
command = "mcp-talk"
env = { MCP_TALK_AUTO_CLEAN_HOURS = "12", MCP_TALK_MAX_MESSAGE_CHARS = "1200" }
// ~/.gemini/settings.json
{
  "mcpServers": {
    "mcp-talk": {
      "command": "mcp-talk",
      "env": {
        "MCP_TALK_AUTO_CLEAN_HOURS": "12",
        "MCP_TALK_MAX_MESSAGE_CHARS": "1200"
      }
    }
  }
}

Example Workflow

  1. Claude sends a task to Gemini:

    send(to="gemini", message="Please review the authentication module", from_agent="claude")
    
  2. Gemini checks for messages:

    chk(agent="gemini")
    
  3. Gemini replies when done:

    reply(id="20251126_143022_abc12345", message="Review complete, LGTM!", from_agent="gemini")
    
  4. Claude receives the reply:

    chk(agent="claude")
    

Development

# Clone and install in development mode
git clone https://github.com/devinvenable/mcp-talk.git
cd mcp-talk
pip install -e .

# Reinstall after changes (if using pipx)
pipx install --force .

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured