Crawlab MCP Server

This is a Model Context Protocol (MCP) server for Crawlab, allowing AI applications to interact with Crawlab's functionality.

Overview

The MCP server provides a standardized way for AI applications to access Crawlab's features, including:

• Spider management (create, read, update, delete)
• Task management (run, cancel, restart)
• File management (read, write)
• Resource access (spiders, tasks)

Architecture

The MCP Server/Client architecture facilitates communication between AI applications and Crawlab:

graph TB User[User] --> Client[MCP Client] Client --> LLM[LLM Provider] Client <--> Server[MCP Server] Server <--> Crawlab[Crawlab API]

subgraph "MCP System"
    Client
    Server
end

subgraph "Crawlab System"
    Crawlab
    DB\[(Database)\]
    Crawlab <\--\> DB
end

class User,LLM,Crawlab,DB external;
class Client,Server internal;

%% Flow annotations
LLM -.-\> |Tool calls| Client
Client -.-\> |Executes tool calls| Server
Server -.-\> |API requests| Crawlab
Crawlab -.-\> |API responses| Server
Server -.-\> |Tool results| Client
Client -.-\> |Human-readable response| User

classDef external fill:#f9f9f9,stroke:#333,stroke-width:1px;
classDef internal fill:#d9edf7,stroke:#31708f,stroke-width:1px;

Communication Flow

1. User Query: The user sends a natural language query to the MCP Client
2. LLM Processing: The Client forwards the query to an LLM provider (e.g., Claude, OpenAI)
3. Tool Selection: The LLM identifies necessary tools and generates tool calls
4. Tool Execution: The Client sends tool calls to the MCP Server
5. API Interaction: The Server executes the corresponding Crawlab API requests
6. Response Generation: Results flow back through the Server to the Client to the LLM
7. User Response: The Client delivers the final human-readable response to the user

Installation and Usage

Option 1: Install as a Python package

You can install the MCP server as a Python package, which provides a convenient CLI:

Install from source

pip install -e .

Or install from GitHub (when available)

pip install git+https://github.com/crawlab-team/crawlab-mcp-server.git

After installation, you can use the CLI:

Start the MCP server

crawlab_mcp-mcp server [--spec PATH_TO_SPEC] [--host HOST] [--port PORT]

Start the MCP client

crawlab_mcp-mcp client SERVER_URL

Option 2: Running Locally

Prerequisites

• Python 3.8+
• Crawlab instance running and accessible
• API token from Crawlab

Configuration

1. Copy the .env.example file to .env:

2. Edit the .env file with your Crawlab API details:

   CRAWLAB_API_BASE_URL=http://your-crawlab-instance:8080/api
   CRAWLAB_API_TOKEN=your_api_token_here
   

Running Locally

1. Install dependencies:

   pip install -r requirements.txt
   

2. Run the server:

Running with Docker

1. Build the Docker image:

   docker build -t crawlab-mcp-server .
   

2. Run the container:

   docker run -p 8000:8000 --env-file .env crawlab-mcp-server
   

Integration with Docker Compose

To add the MCP server to your existing Crawlab Docker Compose setup, add the following service to your docker-compose.yml:

services:

... existing Crawlab services

mcp-server: build: ./backend/mcp-server ports: - "8000:8000" environment: - CRAWLAB_API_BASE_URL=http://backend:8000/api - CRAWLAB_API_TOKEN=your_api_token_here depends_on: - backend

Using with AI Applications

The MCP server enables AI applications to interact with Crawlab through natural language. Following the architecture diagram above, here's how to use the MCP system:

Setting Up the Connection

1. Start the MCP Server: Make sure your MCP server is running and accessible
2. Configure the AI Client: Connect your AI application to the MCP server

Example: Using with Claude Desktop

1. Open Claude Desktop
2. Go to Settings > MCP Servers
3. Add a new server with the URL of your MCP server (e.g., http://localhost:8000)
4. In a conversation with Claude, you can now use Crawlab functionality by describing what you want to do in natural language

Example Interactions

Based on our architecture, here are example interactions with the system:

Create a Spider:

User: "Create a new spider named 'Product Scraper' for the e-commerce project"
↓
LLM identifies intent and calls the create_spider tool
↓
MCP Server executes the API call to Crawlab
↓
Spider is created and details are returned to the user

Run a Task:

User: "Run the 'Product Scraper' spider on all available nodes"
↓
LLM calls the run_spider tool with appropriate parameters
↓
MCP Server sends the command to Crawlab API
↓
Task is started and confirmation is returned to the user

Available Commands

You can interact with the system using natural language commands like:

• "List all my spiders"
• "Create a new spider with these specifications..."
• "Show me the code for the spider named X"
• "Update the file main.py in spider X with this code..."
• "Run spider X and notify me when it's complete"
• "Show me the results of the last run of spider X"

Available Resources and Tools

These are the underlying tools that power the natural language interactions:

Resources

• spiders: List all spiders
• tasks: List all tasks

Tools

Spider Management

• get_spider: Get details of a specific spider
• create_spider: Create a new spider
• update_spider: Update an existing spider
• delete_spider: Delete a spider

Task Management

• get_task: Get details of a specific task
• run_spider: Run a spider
• cancel_task: Cancel a running task
• restart_task: Restart a task
• get_task_logs: Get logs for a task

File Management

• get_spider_files: List files for a spider
• get_spider_file: Get content of a specific file
• save_spider_file: Save content to a file