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agent-coordination-mcp

agent-coordination-mcp

by HappyMonkeyAI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

agent-coordination-mcp

An experimental local MCP server for coordinating installed CLI agents across projects that use file-based task boards, locks, and shared status files.

The goal is not to replace the ai-agent-teamwork workflow. It is to expose that workflow as a small MCP control plane so a primary agent can see which CLI agents are available, assign work, inspect project coordination state, and keep durable assignment records.

Why This Works

The workflow works if the MCP server stays narrow:

  • MCP is the coordination surface, not the editor.

  • Project state remains in plain files owned by each project.

  • CLI agents continue to run as separate tools with their own approval and sandbox behavior.

  • Assignment and heartbeat records are explicit JSON state, not inferred from hidden sessions.

The risky part is supervising long-running CLI processes. This first slice records assignments and generates dispatch intent; later slices can add process launch adapters per CLI once the approval and lifecycle model is clear.

Related MCP server: masterplan

Current Tools

  • list_cli_agents - detect known local CLI agents on PATH.

  • get_project_status - summarize .agent-tasks.json, .agent-manifest.json, and .agent-status.md for a project.

  • list_assignments - read active and historical assignment records.

  • assign_task - record a task assignment to a detected CLI agent.

  • update_assignment_status - update assignment status and notes.

Install

uv sync
uv run agent-coordination-mcp

Dynamic MCP Proxy Entry

Add this to /home/stephen/dynamic-mcp-proxy-server/user.catalogue.json:

{
  "name": "agent-coordination",
  "description": "Local MCP control plane for detecting CLI agents and coordinating file-based project task boards",
  "command": "uv --project /home/stephen/projects/agent-coordination-mcp run agent-coordination-mcp",
  "tags": ["agents", "coordination", "mcp", "cli", "local"],
  "tech_stack": ["python", "mcp", "cli-agents"],
  "runtime": "stdio",
  "env_vars": []
}

Planned Slices

  1. Inventory and assignment tracking.

  2. ai-agent-teamwork task board adapters.

  3. CLI-specific dispatch adapters for opencode, codex, gemini, claude, and other installed agents.

  4. Process/session tracking where supported by the CLI.

  5. Dynamic proxy integration and research tools such as devto-mcp-server.

See docs/ROADMAP.md for the current implementation plan. The next slice is capability-aware inventory before automated dispatch.

Non-Goals

  • No hidden project edits by the MCP server.

  • No generic shell execution tool.

  • No automatic force-unlocking or stale-task rewrites without explicit tool calls.

  • No assumption that every CLI supports resumable sessions.

Available Tools

5 tools
assign_taskC

Record that a CLI agent has been assigned a project task.

Inputs: project_path (required), agent_name (required), task_id (required), instruction (required). Returns: object

get_project_statusC

Summarize ai-agent-teamwork style coordination files for a project.

Inputs: project_path (required). Returns: object

list_assignmentsC

List recorded coordination assignments for a project.

Inputs: project_path (required). Returns: result

list_cli_agentsA

List known CLI agent commands and whether they are available on PATH.

Inputs: extra_commands. Returns: result

update_assignment_statusC

Update a recorded assignment status.

Inputs: project_path (required), assignment_id (required), status (required), note. Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.1/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a distinct action: assigning tasks, retrieving project status, listing assignments, listing CLI agents, and updating assignment status. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (e.g., assign_task, get_project_status), making them predictable and easy to understand.

    Tool Count5/5

    With 5 tools, the set is well-scoped for coordinating CLI agents in projects, covering essential operations without unnecessary complexity.

    Completeness4/5

    The set covers key operations: assign, list, update, and status queries. A minor gap is the lack of a tool to retrieve a single assignment by ID, but the overall surface is adequate for the domain.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Resources

    Related MCP Connectors

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