AI Connect MCP Server

An MCP (Model Context Protocol) server that allows AI agents to query and manage jobs in the AI Connect platform.

About AI Connect Jobs

AI Connect Jobs is a robust asynchronous task management system on the AI Connect platform, enabling the creation, monitoring, and execution of jobs across different platforms like Slack and WhatsApp, with support for scheduled execution, automatic retries, and timeout handling. The API provides endpoints to create, list, query, and cancel jobs, allowing developers and external systems to easily integrate asynchronous processing functionalities into their applications, automating complex workflows without the need to implement the entire task management infrastructure.

Features

This MCP Server provides tools for AI agents to:

• 📋 List Jobs: Query all jobs with advanced filtering
• 🔍 Get Specific Job: Retrieve details of a specific job by ID
• ✅ Create Jobs: Create new jobs for immediate or scheduled execution
• ❌ Cancel Jobs: Cancel running or scheduled jobs
• 📊 Monitor Status: Track job status (WAITING, RUNNING, COMPLETED, FAILED, CANCELED)

Technologies

• Node.js with TypeScript
• Model Context Protocol (MCP) by Anthropic
• Zod for schema validation
• AI Connect API for integration with the Agent Jobs system

Installation

NPX (Recommended)

You can run the MCP server directly using npx without installation:

npx @aiconnect/agentjobs-mcp --help

Local Installation

1. Clone the repository:

git clone <repository-url>
cd agentjobs-mcp

2. Install dependencies:

npm install

3. Configure environment variables (Optional):

The MCP server comes with default values from .env.example, so you can run it without setting any environment variables. However, you must provide an API key for authentication.

cp .env.example .env

Edit the .env file with your credentials:

DEFAULT_ORG_ID=your-organization     # Default: aiconnect
AICONNECT_API_KEY=your-api-key       # Required: Must be provided
AICONNECT_API_URL=https://api.aiconnect.cloud/api/v0  # Default

Important: If no environment variables are provided, the server will use these defaults:

• DEFAULT_ORG_ID: aiconnect
• AICONNECT_API_URL: https://api.aiconnect.cloud/api/v0
• AICONNECT_API_KEY: empty (must be provided for API calls to work)

4. Build the project:

npm run build

Usage

CLI Usage

The MCP server now supports CLI commands for easy management:

# Show help and usage information
npx @aiconnect/agentjobs-mcp --help

# Show version information
npx @aiconnect/agentjobs-mcp --version

# Show current configuration status
npx @aiconnect/agentjobs-mcp --config

# Start MCP server (default behavior)
npx @aiconnect/agentjobs-mcp

Setting Environment Variables:

# Using environment variables with npx
AICONNECT_API_URL=https://api.aiconnect.cloud/api/v0 \
AICONNECT_API_KEY=your-api-key-here \
npx @aiconnect/agentjobs-mcp

# Or create a .env file (recommended for development)
cp .env.example .env
# Edit .env with your credentials
npx @aiconnect/agentjobs-mcp

Required Environment Variables:

• AICONNECT_API_URL: API endpoint URL (e.g., https://api.aiconnect.cloud/api/v0)
• AICONNECT_API_KEY: Your API authentication key

CLI Command Examples:

# Quick help
npx @aiconnect/agentjobs-mcp -h

# Check version
npx @aiconnect/agentjobs-mcp -v

# Verify configuration before starting
npx @aiconnect/agentjobs-mcp -c

# Test with environment variables
env AICONNECT_API_URL=https://api.aiconnect.cloud/api/v0 \
    AICONNECT_API_KEY=test-key \
    npx @aiconnect/agentjobs-mcp --config

Local Development

For local development, you can use npm scripts:

# Build and test CLI commands
npm run cli:help
npm run cli:version  
npm run cli:config

# Run test suite (if available)
npm run test:cli

Configuration Options

This MCP server is designed to work out-of-the-box with minimal configuration. It uses a smart fallback system:

1. With environment variables: Full control over all settings
2. Without environment variables: Uses defaults from .env.example
3. Partial configuration: Mix of environment variables and defaults

Default Values (when no env vars are set):

• DEFAULT_ORG_ID: "aiconnect"
• AICONNECT_API_URL: "https://api.aiconnect.cloud/api/v0"
• AICONNECT_API_KEY: "" (empty - you must provide this)

Error Handling:

• If AICONNECT_API_KEY is not provided, tools will return helpful error messages
• If AICONNECT_API_URL is not set, it defaults to the production API
• If DEFAULT_ORG_ID is not set, it defaults to "aiconnect"

Running the MCP server

npm start

The server will start and wait for connections via stdio transport.

Claude Desktop Configuration

To use this MCP server with Claude Desktop, add the following configuration to your claude_desktop_config.json file:

{
  "mcpServers": {
    "agentjobs": {
      "command": "node",
      "args": ["/path/to/agentjobs-mcp/build/index.js"],
      "env": {
        "DEFAULT_ORG_ID": "your-organization",
        "AICONNECT_API_KEY": "your-api-key",
        "AICONNECT_API_URL": "https://api.aiconnect.cloud/api/v0"
      }
    }
  }
}

Available Tools

🔧 list_jobs

Lists all jobs with filtering and pagination options.

Parameters:

• status (optional): Filter by status (WAITING, RUNNING, COMPLETED, FAILED, CANCELED)
• job_type_id (optional): Filter by job type
• channel_code (optional): Filter by channel code
• limit (optional): Result limit (default: 50)
• offset (optional): Pagination offset
• sort (optional): Field and direction for sorting

🔍 get_job

Gets details of a specific job.

Parameters:

• job_id (required): ID of the job to query

✅ create_job

Creates a new job for execution.

Parameters:

• target_channel: Target channel configuration
• job_type_id: Job type ID
• config: Job configuration (timeouts, retries, etc.)
• params: Job-specific parameters
• scheduled_at (optional): Date/time for scheduled execution
• delay (optional): Random delay in minutes

❌ cancel_job

Cancels a running or scheduled job.

Parameters:

• job_id (required): ID of the job to cancel
• reason (optional): Cancellation reason

Job Status

Jobs can have the following status values:

• WAITING: Job waiting for execution
• SCHEDULED: Job scheduled for future execution
• RUNNING: Job currently running
• COMPLETED: Job completed successfully
• FAILED: Job failed
• CANCELED: Job was canceled

Usage Examples

List running jobs

Agent: "Show me all jobs that are currently running"

Query specific job

Agent: "What's the status of job job-123?"

Create scheduled job

Agent: "Create a daily report job for Slack channel C123456 to run tomorrow at 9 AM"

Cancel job

Agent: "Cancel job job-456 because it's no longer needed"

Project Structure

agentjobs-mcp/
├── src/                    # TypeScript source code
│   ├── index.ts           # Main MCP server
│   ├── cancel_job.ts      # Tool for canceling jobs
│   ├── create_job.ts      # Tool for creating jobs
│   ├── get_job.ts         # Tool for querying job
│   └── list_jobs.ts       # Tool for listing jobs
├── build/                 # Compiled JavaScript code
├── docs/                  # Documentation
│   └── agent-jobs-api.md  # API documentation
├── package.json           # Dependencies and scripts
├── tsconfig.json          # TypeScript configuration
├── .env.example           # Environment variables example
└── README.md              # This file

Development

Available scripts

• npm run build: Compiles TypeScript
• npm start: Runs the compiled server

Adding new tools

1. Create a new file in the src/ folder (e.g., new_tool.ts)
2. Implement the tool following the pattern of existing files
3. Register the tool in src/index.ts
4. Recompile with npm run build

Contributing

1. Fork the project
2. Create a feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License.

Support

For technical support or questions about AI Connect Jobs:

• Check the API documentation
• Contact the AI Connect development team

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Note: This project was developed using the Anthropic mcp-tools scaffold for integration with the AI Connect platform.