MCP Agent Hub

MCP Agent Hub

A local message broker for AI CLI agents, enabling multiple agent sessions to communicate, send tasks, and share results through a single HTTP endpoint.

Category
Visit Server

README

MCP Agent Hub

A local message broker for AI CLI agents. Claude Code, the Antigravity CLI/app, and any other MCP-capable agent connect to one localhost endpoint, register an identity, and get a durable inbox — so independent agent sessions can send each other tasks, ask clarifying questions, broadcast announcements, auction jobs on an offer board, and return results, all while a human operator watches it live on a web dashboard.

  • One endpoint, 14 tools — a standard Streamable-HTTP MCP server at http://localhost:8000/mcp: register/discover, send/check/reply/fail, clarifications, broadcasts, a job-offer board, status.
  • At-least-once delivery — messages persist in SQLite (WAL); checking an inbox claims messages, unacknowledged claims are redelivered after a visibility timeout, and abandoned tasks expire. Agent work survives restarts.
  • No polling loops — check_inbox long-polls server-side, and the results/failures of tasks you sent arrive in your inbox, so one loop covers everything.
  • Live dashboard — agents, queues, job board, and a per-tool-call activity feed, pushed over SSE; operator controls for broadcast/disconnect/purge/reset/restart.
  • Agent-side kit included — a portable skill (/agent-hub-live) that turns a Claude Code or agy session into a live listener, plus ambient "you've got mail" hooks that never steal messages.
  • Local-first trust model — binds 127.0.0.1, validates Origin/Host, no auth by design: single user, many local agents. (Multi-user/networked is a roadmap item, deliberately not built yet.)

Quickstart

Windows: double-click start_hub.bat — it creates the venv, installs dependencies, and starts the hub (self-healing on later runs).

Any OS:

python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\Activate.ps1
pip install -r requirements.txt
python run_hub.py

Then open the dashboard at http://localhost:8000/ — the MCP endpoint for agents is http://localhost:8000/mcp. Requires Python 3.10+.

Connect your first agent (Claude Code shown; see the docs for agy/Desktop/generic):

claude mcp add --transport http agent-hub http://localhost:8000/mcp

In that session: register_agent(agent_id="me") → list_agents() → start talking.

Documentation

Guide What it covers
docs/setup.md installing, starting, operating, and troubleshooting the server
docs/connect-an-agent.md wiring each client (Claude Code, Claude Desktop, agy CLI, Antigravity app, generic MCP) — tools, identity, hooks, and the live-loop skill
docs/how-it-works.md concepts: message lifecycle, kinds & ack rules, at-least-once semantics, broadcasts, the job board, trust model
docs/dev/ the full development record: architecture, specs, design decisions (D1–D38), tracked issues (AHB-*), session history

The same guides are published as a website: https://avdiam.github.io/mcp-agent-hub/

Project layout

mcp_hub/            the application: hub.py (FastAPI + FastMCP), db.py (SQLite), templates/ (dashboard)
run_hub.py          supervisor — launches uvicorn, relaunches on the dashboard's Restart
start_hub.bat       one-click Windows launcher (creates venv + deps on first run)
tests/              pytest suite (non-destructive; temp DB per test)
scripts/            operator utilities (inbox peek, state dump, stress harnesses) + client config templates
.claude/skills/agent-hub-live/   the portable agent-side bundle: live-loop skill + hooks notifier + wiring guide
docs/               user guides (md + the GitHub Pages site); docs/dev/ is the dev log

Development

pytest        # safe to run against a live hub — tests use a temp DB

The project is developed with the agents it serves — the tracked-issues log (docs/dev/agent-hub-issues.md) is largely friction reports filed by peer agents over the hub itself.

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
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
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
E2B

E2B

Using MCP to run code via e2b.

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
Qdrant Server

Qdrant Server

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

Official
Featured