setup-fleet-mcp 0.7.4

Fleet management using MCP for AI agents in Coder workspaces

Fleet MCP

Fleet management using MCP for AI agents in Coder workspaces.

Overview

Fleet MCP provides MCP (Model Context Protocol) tools for managing Claude Code agent fleets running in Coder workspaces. It follows clean architecture principles with strict layer separation for maintainability and testability.

Architecture

The project uses a 5-layer clean architecture with unidirectional dependencies:

┌─────────────────────────────────────────────────────────────┐
│  Layer 1: MCP Tools (FastMCP Entry Points)                 │
│  Files: tools/list_agents.py, create_agent.py, etc.        │
│  Responsibility: MCP protocol, input validation            │
└──────────────────┬──────────────────────────────────────────┘
                   │ depends on
                   ▼
┌─────────────────────────────────────────────────────────────┐
│  Layer 2: Services (Business Logic)                        │
│  Files: services/agent_service.py, task_service.py         │
│  Responsibility: Business rules, orchestration              │
└──────────────────┬──────────────────────────────────────────┘
                   │ depends on
                   ▼
┌─────────────────────────────────────────────────────────────┐
│  Layer 3: Repositories (Data Access)                       │
│  Files: repositories/agent_repository.py, etc.             │
│  Responsibility: Entity transformation, data access         │
└──────────────────┬──────────────────────────────────────────┘
                   │ depends on
                   ▼
┌─────────────────────────────────────────────────────────────┐
│  Layer 4: Clients (HTTP Communication)                     │
│  Files: clients/coder_client.py                            │
│  Responsibility: HTTP requests, error handling              │
└──────────────────┬──────────────────────────────────────────┘
                   │ uses
                   ▼
┌─────────────────────────────────────────────────────────────┐
│  External: Coder API                                        │
│  Endpoints: workspaces, templates, presets, tasks           │
└─────────────────────────────────────────────────────────────┘

         ┌──────────────────────────────┐
         │  Shared: Models (Pydantic)   │
         │  Files: models/*.py          │
         │  Used by: All layers         │
         └──────────────────────────────┘

Layer Responsibilities

1. Tools Layer (tools/): MCP tool entry points with FastMCP, parameter validation
2. Services Layer (services/): Business logic, orchestration, rule enforcement
3. Repositories Layer (repositories/): Data access patterns, entity transformation
4. Clients Layer (clients/): HTTP communication with Coder API, error handling
5. Models Layer (models/): Shared Pydantic domain entities, validation

Installation

This project uses uv for dependency management. Ensure you have uv installed:

# Install dependencies
cd libs/fleet-mcp
uv sync

# Install with dev dependencies
uv sync --all-extras

Configuration

Copy .env.example to .env and configure:

cp .env.example .env
# Edit .env with your Coder instance URL and session token

Authentication (Optional)

Fleet MCP supports header-based Bearer token authentication for secure deployment. Authentication is disabled by default and can be enabled via environment variables.

Enable Authentication

# In your .env file
FLEET_MCP_AUTH_ENABLED=true

# Optional: Custom token file location (default: ~/.fleet-mcp/auth_token)
FLEET_MCP_AUTH_TOKEN_FILE=/custom/path/auth_token

Token Generation

On first startup with authentication enabled, the server will:

1. Generate a cryptographically secure access token (256-bit entropy)
2. Store it in ~/.fleet-mcp/auth_token (file permissions: 0600)
3. Log the token to stdout for distribution

The token persists across server restarts - it won't be regenerated unless the file is deleted.

Using the Token

Configure your MCP client with the Authorization header:

{
  "mcpServers": {
    "fleet-mcp": {
      "url": "https://fleet-mcp.example.com",
      "headers": {
        "Authorization": "Bearer <your-token-here>"
      }
    }
  }
}

Retrieve the Token

# From the token file
cat ~/.fleet-mcp/auth_token | jq -r '.value'

# Or from server logs (shown on first startup)

Rotate the Token

To generate a new token:

# Stop the server
# Delete the token file
rm ~/.fleet-mcp/auth_token
# Restart the server (generates new token)

For more details, see the authentication documentation.

Running the Server

# Using Nx
nx server fleet-mcp

# Or directly with uv
cd libs/fleet-mcp
uv run fastmcp run src/fleet_mcp/__main__.py

Testing

# Run tests with Nx
nx test fleet-mcp

# Or directly with uv
cd libs/fleet-mcp
uv run pytest

# With coverage
uv run pytest --cov=fleet_mcp --cov-report=term-missing

# Verify layer boundaries with import-linter
nx lint-imports fleet-mcp

# Run all validation together
nx run-many -t test lint-imports -p fleet-mcp

Available MCP Tools

Agent Discovery & Inspection

• list_agents: List all agents with optional filtering by status and project
• show_agent: Show detailed information about a specific agent
• list_agent_projects: List available projects (templates)
• list_agent_roles: List available roles for a project

Agent Lifecycle

• create_agent: Create a new agent with specified name, project, role, and task
• delete_agent: Delete an agent and destroy its workspace
• restart_agent: Restart an agent workspace to refresh environment

Task Management

• start_agent_task: Assign a task to an idle agent
• cancel_agent_task: Cancel a running task by sending Ctrl+C interrupt

History & Logs

• show_agent_task_history: View paginated task history (ordered newest first)
• show_agent_log: View paginated conversation logs (default: latest entry only)

Usage Examples

Example 1: List All Agents

# Using MCP client
result = await client.call_tool("list_agents", {})
print(result["agents"])
# [{"name": "agent-1", "status": "idle", "project": "Setup", ...}, ...]

Example 2: Create a New Agent

result = await client.call_tool("create_agent", {
    "name": "data-scientist",
    "project": "Setup",
    "role": "coder",
    "task": "Analyze sales data from Q4"
})

print(result["agent"]["workspace_id"])
# "workspace-uuid-here"

Example 3: Send Task to Agent

# First, verify agent is idle
agent = await client.call_tool("show_agent", {
    "agent_name": "data-scientist"
})

if agent["agent"]["status"] == "idle":
    result = await client.call_tool("start_agent_task", {
        "agent_name": "data-scientist",
        "task_description": "Generate sales report for Q4"
    })
    print(result["message"])
    # "Task assigned to agent 'data-scientist'"

Example 4: View Task History

result = await client.call_tool("show_agent_task_history", {
    "agent_name": "data-scientist",
    "page": 1,
    "page_size": 10
})

for task in result["tasks"]:
    print(f"{task['created_at']}: {task['message']}")

Development

Project Structure

libs/fleet-mcp/
├── src/fleet_mcp/
│   ├── __init__.py
│   ├── __main__.py          # FastMCP server entry point
│   ├── models/              # Pydantic domain models
│   ├── clients/             # Coder API HTTP client
│   ├── repositories/        # Data access layer
│   ├── services/            # Business logic layer
│   └── tools/               # MCP tool definitions
└── tests/
    ├── fixtures/            # Test data and mocks
    ├── cassettes/           # VCR cassettes (if used)
    ├── clients/             # Client layer tests
    ├── repositories/        # Repository layer tests
    ├── services/            # Service layer tests
    └── tools/               # Tool layer tests

Layer Responsibilities

• Tools: Parameter validation, MCP protocol handling, response formatting
• Services: Business rules, cross-cutting concerns, orchestration
• Repositories: Entity mapping, data access patterns
• Clients: HTTP requests, error handling, retries
• Models: Data validation, serialization, type safety

License

See repository root for license information.