Agent Link Mcp

agent-link-mcp

[](https://www.npmjs.com/package/agent-link-mcp) [](https://opensource.org/licenses/MIT)

English | [한국어](README.ko.md)

MCP server for bidirectional AI agent collaboration. Spawn and communicate with any AI coding agent CLI — Claude Code, Codex, Gemini, Aider, and more.

When to Use

• Stuck on a bug? — Your agent tried twice and failed. Let it ask another agent for a fresh perspective.
• Need a second opinion? — Get code review or architectural advice from a different AI model.
• Cross-model strengths — Use Claude for planning, Codex for execution, Gemini for research.
• Parallel work — Spawn multiple agents to tackle independent subtasks simultaneously.
• Rubber duck debugging — Have one agent explain the problem to another and get back a solution.

Use Cases

Get Help When Stuck

Your primary agent keeps failing on the same issue? Ask another agent:

# Claude Code is stuck on a TypeScript error it can't resolve.
# It spawns Codex for a second opinion:

spawn_agent("codex", "This TypeScript error keeps appearing. How do I fix it?", {
  error: "Type 'string' is not assignable to type 'number'",
  files: ["src/utils.ts"]
})

Cross-Agent Code Review

Have another model review your agent's code changes:

spawn_agent("claude", "Review these changes for bugs and edge cases", {
  files: ["src/api.ts", "src/handler.ts"],
  intent: "Code review before merge"
})

Multi-Agent Pipeline

Build a pipeline where agents handle different stages:

# Agent 1: Research
spawn_agent("gemini", "Find the best approach for WebSocket reconnection")

# Agent 2: Implementation (using Agent 1's advice)
spawn_agent("codex", "Implement WebSocket reconnection with exponential backoff", {
  files: ["src/ws-client.ts"]
})

# Agent 3: Review
spawn_agent("claude", "Review this implementation for production readiness", {
  files: ["src/ws-client.ts"]
})

Bidirectional Collaboration

Agents can ask questions back. The host answers, and work continues:

Host: spawn_agent("codex", "Add caching to the API layer")
Codex: [QUESTION] Should I use Redis or in-memory cache?
Host: reply("codex-a1b2c3", "Use Redis, we have it in our docker-compose")
Codex: [RESULT] Added Redis caching with 5-minute TTL...

Why

AI coding agents get stuck sometimes. Instead of waiting for you, they can ask another agent for help. agent-link-mcp lets any MCP-compatible agent spawn other agent CLIs as collaborators, exchange questions, and get results back — all through standard MCP tools.

• One-side install — only the host agent needs this MCP server. Spawned agents are just CLI subprocesses.
• Bidirectional — the host can ask questions to the spawned agent, and the spawned agent can ask questions back.
• Any agent — works with any CLI that accepts a prompt and returns text. Built-in profiles for Claude, Codex, Gemini, and Aider.
• Multi-agent — spawn multiple agents simultaneously for parallel collaboration.

Prerequisites

agent-link-mcp spawns other AI agents as CLI subprocesses. You need to install and authenticate the agent CLIs you want to collaborate with:

| Agent | Install | Auth | |-------|---------|------| | Claude Code | npm install -g @anthropic-ai/claude-code | claude login | | Codex | npm install -g @openai/codex | codex login | | Gemini CLI | npm install -g @anthropic-ai/gemini-cli | gemini login | | Aider | pip install aider-chat | Set OPENAI_API_KEY or ANTHROPIC_API_KEY |

You only need the ones you plan to use. agent-link-mcp auto-detects which CLIs are installed.

Install

# Claude Code
claude mcp add agent-link npx agent-link-mcp

# Codex
codex mcp add agent-link npx agent-link-mcp

# Any MCP client
npx agent-link-mcp

> Note: Only the agent you're working in needs this MCP server installed. The other agents are spawned as subprocesses — they don't need agent-link-mcp.

Tools

spawn_agent

Spawn an agent and send it a task.

{
  "agent": "codex",
  "task": "Refactor this function for better performance",
  "context": {
    "files": ["src/utils.ts"],
    "error": "TypeError: Cannot read property 'x' of undefined",
    "intent": "Performance improvement"
  },
  "model": "o3",
  "timeoutMs": 7200000
}

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | agent | string | required | Agent name ("claude", "codex", "gemini", "aider") | | task | string | required | Task description | | context | object | — | Optional { files, error, intent, diff }. diff: true includes git diff output. diff: "staged" for staged only. | | cwd | string | cwd | Working directory for the agent process | | model | string | — | Model to use (e.g. "o3", "gpt-5.4", "claude-sonnet-4", "gemini-2.5-pro"). Passed via --model flag. | | thinking | string | — | Thinking/reasoning depth ("low", "medium", "high", "max"). Claude: --effort, Codex: -c reasoning_effort, Aider: --reasoning-effort. | | retry | boolean | false | Auto-retry on failure (up to 3 attempts). | | escalate | boolean | false | On retry, automatically increase thinking level. Requires retry: true. | | timeoutMs | number | 3600000 | Timeout in ms. Default: 1 hour. |

Returns one of:

• { status: "done", agentId: "codex-a1b2c3", result: "..." } — task completed
• { status: "waiting_for_reply", agentId: "codex-a1b2c3", question: "..." } — agent needs clarification
• { error: "...", agentId: "codex-a1b2c3" } — something went wrong

spawn_agents

Run multiple agents in parallel. Returns all results together.

{
  "agents": [
    { "agent": "codex", "task": "Review for bugs", "context": { "diff": true } },
    { "agent": "claude", "task": "Review for security", "context": { "diff": true } }
  ],
  "cwd": "/path/to/project"
}

Returns { summary: { total, succeeded, failed, waiting }, results: [...] }.

reply

Answer a spawned agent's question and continue the conversation.

{
  "agentId": "codex-a1b2c3",
  "message": "Yes, you can remove the side effects"
}

kill_agent

Abort a running agent session.

{
  "agentId": "codex-a1b2c3"
}

list_agents

List available agent CLIs.

{
  "agents": [
    { "name": "claude", "command": "claude", "source": "auto", "available": true },
    { "name": "codex", "command": "codex", "source": "auto", "available": true },
    { "name": "gemini", "command": "gemini", "source": "auto", "available": false }
  ]
}

get_status

Get active agent sessions.

{
  "sessions": [
    { "agentId": "codex-a1b2c3", "agent": "codex", "status": "waiting_for_reply", "startedAt": "..." }
  ]
}

How It Works

You (using Claude Code)
  ↓
"Ask Codex to help with this refactoring"
  ↓
Claude Code → spawn_agent("codex", task, context)
  ↓
agent-link-mcp server → spawns `codex` CLI as subprocess
  ↓
Codex processes the task...
  ↓
Codex: "[QUESTION] Should I remove the side effects?"
  ↓
agent-link-mcp → parses response → returns to Claude Code
  ↓
Claude Code → reply("codex-a1b2c3", "Yes, remove them")
  ↓
agent-link-mcp → re-invokes Codex with accumulated context
  ↓
Codex: "[RESULT] Refactoring complete. Here's what I changed..."
  ↓
Claude Code receives the result and continues working

Configuration

Auto-detection

agent-link-mcp automatically detects installed agent CLIs:

| Agent | CLI Command | |-------|------------| | Claude Code | claude | | Codex | codex | | Gemini | gemini | | Aider | aider |

Custom agents

Add custom agents via config file at ~/.agent-link/config.json:

{
  "agents": {
    "codex": {
      "command": "/usr/local/bin/codex",
      "args": ["--full-auto"],
      "promptFlag": null,
      "outputFormat": "text"
    },
    "my-local-llm": {
      "command": "ollama",
      "args": ["run", "codellama"],
      "promptFlag": null,
      "outputFormat": "text"
    }
  }
}

Override config path with AGENT_LINK_CONFIG environment variable.

Model Selection

You can specify which model the spawned agent should use via the model parameter:

# Use a specific model for Codex
spawn_agent("codex", "Debug this issue", { model: "o3" })

# Use a specific model for Claude
spawn_agent("claude", "Review this code", { model: "claude-sonnet-4" })

The model name is passed to the agent CLI via its --model flag. If omitted, the agent uses its default model.

Thinking / Reasoning Depth

Control how deeply the agent reasons with the thinking parameter:

# High reasoning for complex debugging
spawn_agent("codex", "Debug this race condition", { thinking: "high" })

# Max effort for Claude
spawn_agent("claude", "Architect a new auth system", { thinking: "max" })

| Agent | Flag | Values | |-------|------|--------| | Claude | --effort | low, medium, high, max | | Codex | -c reasoning_effort | low, medium, high | | Aider | --reasoning-effort | low, medium, high |

If omitted, the agent uses its default reasoning level.

Timeout

Default timeout is 1 hour (3,600,000ms). You can override per-call:

# 2 hour timeout for complex tasks
spawn_agent("codex", "Refactor the entire auth system", { timeoutMs: 7200000 })

Conversation Protocol

Spawned agents receive instructions to format their responses:

• [QUESTION] ... — needs clarification from the host agent
• [RESULT] ... — task completed

If the agent doesn't follow the format, the entire output is treated as a result.

License

MIT

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mikusnuz
• Source: mikusnuz/agent-link-mcp
• License: MIT

Install and usage instructions live in the source repository linked above.