mcp-acp-bridge

A Model Context Protocol (MCP) server that provides a bridge to interact with any Agent Client Protocol (ACP) compatible agent (like Gemini CLI, Claude Code, OpenCode, Codex, Pi, Aider, etc.) through a stateful tool-based interface.

Features

• Multi-Agent Support: Connect to and manage multiple ACP agents concurrently using unique connection IDs.
• Flexible Directories: Each agent connection can operate in a different working directory, perfect for multi-tasking across projects.
• Background Generation: Supports asynchronous prompt processing, allowing you to send prompts to multiple agents and read their responses as they become available.
• Tool Approval Control: Manual or automatic approval of agent tool calls. In manual mode, tool calls are held until explicitly approved or denied.
• Incremental Response Reading: Read only new content since the last read, enabling efficient streaming for long-running tasks.
• Tool Call Details: Inspect detailed tool call information — which tools were called, their status, duration, and output.
• Session Reset: Reset an agent's conversation context without restarting the process, for clean multi-task sessions.
• Process Health Tracking: Detect when an agent process exits and surface this to the orchestrator.
• Native SDK Integration: Built using the official @agentclientprotocol/sdk and @modelcontextprotocol/sdk.

Installation

From NPM (Global)

npm install -g @theorionic/mcp-acp-bridge

From Source

The project prefers Bun but will fall back to NPM/Node if Bun is not present.

# If you have Bun
bun install
bun run build

# Or using NPM
npm install
npm run build

Supported Agents & Configuration

The bridge comes preconfigured with several popular AI agents. Below is a guide on how to add this MCP server to different environments and the required setup for each agent.

1. General MCP Configuration

To use this bridge in your IDE (like Zed, Cursor, or VS Code with an MCP extension), add it to your configuration file:

{
  "mcpServers": {
    "acp-bridge": {
      "command": "npx",
      "args": ["-y", "@theorionic/mcp-acp-bridge"]
    }
  }
}

2. Preconfigured Agent Details

When calling initialize_client, use the following agent keys:

3. Adding to Specific AI Agents

Gemini CLI

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "acp-bridge": {
      "command": "npx",
      "args": ["-y", "@theorionic/mcp-acp-bridge"]
    }
  }
}

Claude (Desktop/Web)

Currently, custom MCP servers are primarily supported in the Claude Desktop app. Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "acp-bridge": {
      "command": "npx",
      "args": ["-y", "@theorionic/mcp-acp-bridge"]
    }
  }
}

Zed Editor

Add to ~/.config/zed/settings.json:

{
  "context_servers": [
    {
      "name": "acp-bridge",
      "command": "npx",
      "args": ["-y", "@theorionic/mcp-acp-bridge"]
    }
  ]
}

Usage

Running the Server

If installed globally via NPM:

mcp-acp-bridge

From source (automatically detects Bun or Node):

npm start

MCP Tools

1. initialize_client

Initializes a new ACP connection using a preconfigured agent.

Parameters:

• agent (string, required): The preconfigured agent to connect to (e.g., gemini, opencode).
• connectionId (string, required): A unique identifier you choose for this connection (e.g., "frontend-dev", "docs-agent").
• cwd (string): Working directory for this specific agent.
• env (object): Environment variables for the agent process.
• authMethodId (string): Auth method ID (e.g., oauth-personal).
• extraArgs (array of strings): Additional CLI arguments (e.g., ["--model", "gemini-2.0-flash"]).
• toolApprovalMode (string: "auto" | "manual"): Controls how tool call permissions are handled. Default: "auto".
  • "auto": All tool calls are automatically approved (original behavior).
  • "manual": Tool calls are held until explicitly approved or denied via approve_tool_call.

2. send_prompt

Sends a prompt to a specific connected agent.

Parameters:

• connectionId (string, required): The ID of the connection to use.
• prompt (string, required): The message to send to the agent.
• mode (string: "wait" | "poll" | "interrupt"): How to handle the prompt.
  • wait: Block until the full response is generated.
  • poll: Start generation and return immediately.
  • interrupt: Stop current generation and send a new prompt.
• preserveHistory (boolean): If true, append to the existing response buffer instead of clearing it. Useful for multi-turn conversations where you need prior context. Default: false.

3. read_response

Reads the response from a specific agent. Supports incremental reads and detailed tool call information.

Parameters:

• connectionId (string, required): The ID of the connection to read from.
• mode (string: "wait" | "poll"): wait blocks until generation completes, poll returns immediately.
• sinceLastRead (boolean): If true, only returns content added since the last read_response call (incremental mode). Useful for monitoring long-running tasks without re-reading the full buffer. Default: false.
• includeToolDetails (boolean): If true, includes detailed tool call information (tool name, status, duration, content) instead of just activity log summaries. Default: false.

4. approve_tool_call

Approve or deny a pending tool call permission request from an agent. Only applicable when the connection's toolApprovalMode is "manual".

Parameters:

• connectionId (string, required): The ID of the connection.
• requestId (string, required): The ID of the pending permission request (visible in read_response or list_connections output).
• action (string: "approve" | "deny"): Whether to approve or deny the permission request.
• optionIndex (integer): When approving, which option to select (default: 0, typically "allow"). Use list_connections to see available options.

5. reset_session

Reset the agent's conversation session on an existing connection. Clears conversation history and response buffers without restarting the agent process. Much faster than closing and re-initializing.

Parameters:

• connectionId (string, required): The ID of the connection to reset.
• cwd (string): Optional new working directory for the reset session.

6. list_connections

Lists all active ACP connections with detailed status information including pending permission requests, tool call counts, and process health.

7. close_connection

Closes a specific ACP connection and terminates the underlying agent process.

Parameters:

• connectionId (string, required): The ID of the connection to close.

Orchestration Pattern Example

Here's a typical pattern for orchestrating a coding agent:

1. initialize_client(agent="gemini", connectionId="coder", toolApprovalMode="manual")
2. send_prompt(connectionId="coder", prompt="Implement the auth module")
3. read_response(connectionId="coder", mode="poll", includeToolDetails=true)
   → See pending tool calls, approve/deny as needed
4. approve_tool_call(connectionId="coder", requestId="perm_123", action="approve")
5. read_response(connectionId="coder", mode="wait", sinceLastRead=true, includeToolDetails=true)
   → Get only new content, with tool call details
6. reset_session(connectionId="coder")
   → Clean slate for next task, no process restart needed
7. send_prompt(connectionId="coder", prompt="Now implement the tests")
...
8. close_connection(connectionId="coder")

Configuration for Gemini CLI

To use this MCP server in your local project with the Gemini CLI, add the following to your .gemini/settings.json:

{
  "mcpServers": {
    "acp-gemini": {
      "command": "bun",
      "args": ["run", "/path/to/your/project/src/index.ts"]
    }
  }
}

Development

This project uses:

• @modelcontextprotocol/sdk for MCP server implementation.
• @agentclientprotocol/sdk for ACP client communication.
• child_process.spawn to manage the underlying agent process.

License

Apache-2.0