Skip to main content
Glama
vkolotov

mcp-agent-collaboration

by vkolotov

MCP Agent Collaboration

In-memory MCP server for cooperative collaboration between separate agents.

The intended workflow is:

  1. A coordinator/main agent starts this MCP server as a dedicated Streamable HTTP process.

  2. The coordinator creates a topic and joins it.

  3. Secondary agents join the same topic with friendly names such as builder or reviewer.

  4. Agents exchange direct and broadcast messages through MCP tools.

  5. Idle agents long-poll with read_messages.

  6. Working agents use check_in to report progress and read messages in one call.

Messages are held only in memory. Restarting the server clears all topics, members, and messages. After a restart, old join_token values are invalid and agents must re-join.

Install

python3 -m venv .venv
. .venv/bin/activate
pip install -e .

Related MCP server: junto-memory

Run

mcp-agent-collaboration --host 127.0.0.1 --port 8000

The MCP endpoint is:

http://127.0.0.1:8000/mcp

For Codex, add a Streamable HTTP MCP server in config.toml:

[mcp_servers.agent_collaboration]
url = "http://127.0.0.1:8000/mcp"
tool_timeout_sec = 600

Set tool_timeout_sec high enough for your preferred long-poll duration. The server itself does not impose a maximum wait; the MCP client/tool runtime may still have its own timeout.

Codex Autostart

For Codex, the preferred setup is the stdio autostart proxy. Codex launches the proxy as a normal stdio MCP server; the proxy starts the shared Streamable HTTP server on localhost if it is not already running, then forwards tool calls to it.

From this source checkout:

codex mcp add agent_collaboration \
  --env PYTHONPATH=/home/vkolotoff/projects/mcp-agent-collaboration/src \
  -- python3 -m mcp_agent_collaboration.autostart_stdio

After installing the package, this shorter form is enough:

codex mcp add agent_collaboration -- mcp-agent-collaboration-stdio

Defaults:

  • MCP_AGENT_COLLAB_HOST=127.0.0.1

  • MCP_AGENT_COLLAB_PORT=8000

  • MCP_AGENT_COLLAB_PATH=/mcp

  • MCP_AGENT_COLLAB_LOG=/tmp/mcp-agent-collaboration.log

Override them with --env only when needed.

To install the package into your user-level Python environment:

python3 -m pip install --user /home/vkolotoff/projects/mcp-agent-collaboration
codex mcp add agent_collaboration -- mcp-agent-collaboration-stdio

codex mcp add writes to the global Codex MCP config by default, so the server is available to future Codex sessions after restart.

Tools

create_topic

Create a topic by string name.

{
  "topic": "build-123"
}

join_topic

Join a topic with a friendly agent name. The returned join_token is required for message operations.

{
  "topic": "build-123",
  "agent_name": "reviewer",
  "role": "secondary",
  "create_if_missing": true
}

Agent names are unique within a topic. The literal name all is reserved for broadcast messages.

send_message

Send a direct message to one agent or a broadcast to all agents currently joined.

{
  "join_token": "opaque-token",
  "recipient": "reviewer",
  "body": {
    "type": "review_request",
    "task_id": "task-001",
    "summary": "Implementation is ready for review."
  }
}

Use "recipient": "all" for broadcast. Broadcast recipients are snapshotted at send time, so agents who join later do not receive older broadcasts. The sender receives its own broadcast by default because it is also a joined agent; set include_self to false to opt out.

Compact output is the default:

{
  "id": "msg_123",
  "stored": true
}

Pass "verbosity": "full" only when you need topic, sender, and recipient metadata.

read_messages

Read and consume pending messages for the joined agent.

{
  "join_token": "opaque-token",
  "timeout_ms": 600000,
  "max_messages": 20
}

Compact output is the default:

{
  "timed_out": false,
  "messages": [
    {
      "id": "msg_123",
      "from": "reviewer",
      "body": {
        "type": "review_result",
        "status": "approved"
      }
    }
  ]
}

Pass "verbosity": "full" only when you need topic, recipient, timestamp, or broadcast snapshot metadata.

Long-poll behavior:

  • If messages are already pending, return immediately.

  • If no messages are pending and timeout_ms > 0, hold the request open until a relevant message arrives or the requested timeout elapses.

  • If timeout_ms is 0, return immediately with pending messages or an empty timeout response.

  • The server does not impose its own maximum timeout.

  • Returned messages are consumed.

Deletion behavior:

  • Direct messages are deleted after the recipient reads them.

  • Broadcast messages are deleted after every send-time recipient has read them.

  • If an agent leaves a topic, it is removed from unread recipient sets so old broadcasts can be cleaned up.

check_in

Optionally send a message and read pending messages in one tool call. This is the preferred work-loop tool for secondary agents because it avoids a separate send_message call followed by read_messages.

{
  "join_token": "opaque-token",
  "timeout_ms": 0,
  "recipient": "coordinator",
  "body": {
    "type": "progress",
    "task_id": "task-001",
    "done": "Added compact read tests.",
    "next": "Update docs.",
    "blockers": []
  }
}

Compact output:

{
  "sent": {
    "id": "msg_123",
    "stored": true
  },
  "timed_out": true,
  "messages": []
}

Omit body to use check_in as a compact read. Use a long timeout_ms when idle, and timeout_ms: 0 or a short timeout between work chunks. body: null is treated the same as omitting body, so it cannot be used as a sent message payload. If body is provided with a large timeout_ms, the send confirmation is returned only when the read side wakes or times out; use timeout_ms: 0 for fire-and-return progress updates.

leave_topic

Leave a topic and stop receiving future messages.

{
  "join_token": "opaque-token"
}

list_topics

List topics with member and pending-message counts.

list_topic_members

List members in a topic.

Collaboration Contract

This MCP does not forcibly interrupt running agents. Messages wake agents that are currently blocked in read_messages; agents that are actively working see messages at their next check-in.

Secondary agents must:

  • Join with a clear role name.

  • Send a readiness message to the coordinator.

  • Work in bounded chunks.

  • Use check_in to send progress to the coordinator and read messages at reasonable intervals.

  • Always poll after completing a work chunk, after sending a review result, and after reporting task completion.

  • Treat urgent or cancellation messages as priority instructions when seen.

  • Send task_complete when done.

  • Ask the coordinator for more work, then enter idle long-poll mode.

The coordinator must:

  • Start the server.

  • Create the topic.

  • Join as coordinator or another clear main-agent name.

  • Assign tasks to secondary agents.

  • Watch progress, blockers, completion messages, and requests for more work.

  • Poll after sending assignments, clarifications, cancellations, or follow-up work so queued replies are not missed.

Recommended message body types:

  • presence

  • task_assignment

  • progress

  • task_complete

  • request_more_work

  • review_request

  • review_result

  • cancel_task

  • interrupt

interrupt is cooperative. It is not forced preemption.

Test

python3 -m unittest discover -s tests
F
license - not found
-
quality - not tested
C
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/vkolotov/mcp-agent-collaboration'

If you have feedback or need assistance with the MCP directory API, please join our Discord server