Skip to main content

MCP Server for Ambivo API endpoints - Natural language queries and direct entity data access

Project description

Add to Cursor Add to VS Code Add to Claude Add to ChatGPT Add to Codex Add to Gemini

Ambivo Claude MCP Server

This Claude MCP (Model Context Protocol) server provides access to Ambivo API endpoints for natural language querying of entity data with Claude AI.

Features

  • Natural Language Queries: Execute natural language queries against entity data using the /entity/natural_query endpoint
  • JWT Authentication: Secure access using Bearer token authentication
  • Rate Limiting: Built-in rate limiting to prevent API abuse
  • Token Caching: Efficient token validation with caching
  • Error Handling: Comprehensive error handling with detailed error messages
  • Retry Logic: Automatic retry with exponential backoff for failed requests

Tools

1. set_auth_token

Set the JWT Bearer token for authentication with the Ambivo API.

Parameters:

  • token (string, required): JWT Bearer token

Usage:

{
  "token": "your-jwt-token-here"
}

2. natural_query

Execute natural language queries against Ambivo entity data.

Parameters:

  • query (string, required): Natural language query describing what data you want
  • response_format (string, optional): Response format - "table", "natural", or "both" (default: "both")

Example queries:

  • "Show me leads created this week"
  • "Find contacts with gmail addresses"
  • "List opportunities worth more than $10,000"
  • "Show me leads with attribution_source google_ads from the last 7 days"

Usage:

{
  "query": "Show me leads created this week with attribution_source google_ads",
  "response_format": "both"
}

About

This is a pure Claude-based MCP server implementation for the Ambivo API, designed to work seamlessly with Claude Desktop and other Claude-compatible MCP clients. It enables natural language interaction with your Ambivo CRM data through Claude's powerful language understanding capabilities.

Installation

Option 1: Install from PyPI (Recommended)

pip install ambivo-mcp-server

Option 2: Install from Source

git clone https://github.com/ambivo-corp/ambivo-mcp-server.git
cd ambivo-mcp-server
pip install -e .

Running the Server

# If installed via pip
ambivo-mcp-server

# Or using Python module
python -m ambivo_mcp_server.server

Configuration

The server uses the following default configuration:

  • Base URL: https://goferapi.ambivo.com
  • Timeout: 30 seconds
  • Content Type: application/json

You can modify these settings in the AmbivoAPIClient class if needed.

Authentication

  1. First, set your authentication token using the set_auth_token tool
  2. The token will be included in all subsequent API requests as a Bearer token
  3. The token should be a valid JWT token from your Ambivo API authentication

Error Handling

The server provides comprehensive error handling:

  • Authentication errors: Clear messages when token is missing or invalid
  • HTTP errors: Detailed HTTP status codes and response messages
  • Validation errors: Parameter validation with helpful error messages
  • Network errors: Timeout and connection error handling

API Endpoints

This MCP server interfaces with these Ambivo API endpoints:

/entity/natural_query

  • Method: POST
  • Purpose: Process natural language queries for entity data retrieval
  • Authentication: Required (JWT Bearer token)
  • Content-Type: application/json

/entity/data

  • Method: POST
  • Purpose: Direct entity data access with structured parameters
  • Authentication: Required (JWT Bearer token)
  • Content-Type: application/json

Example Workflow

  1. Set Authentication:

    {
      "tool": "set_auth_token",
      "arguments": {
        "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
      }
    }
    
  2. Natural Language Query:

    {
      "tool": "natural_query", 
      "arguments": {
        "query": "Show me all leads created in the last 30 days with phone numbers",
        "response_format": "both"
      }
    }
    
  3. Direct Entity Query:

    {
      "tool": "entity_data",
      "arguments": {
        "entity_type": "contact",
        "filters": {"email": {"$regex": "@gmail.com$"}},
        "limit": 100,
        "sort": {"created_date": -1}
      }
    }
    

Development

To extend this MCP server:

  1. Add new tools: Implement additional tools in the handle_list_tools() and handle_call_tool() functions
  2. Modify API client: Extend the AmbivoAPIClient class to support additional endpoints
  3. Update configuration: Modify default settings in the configuration section

Troubleshooting

Common Issues:

  1. "Authentication required" error: Ensure you've called set_auth_token first
  2. HTTP 401/403 errors: Verify your JWT token is valid and not expired
  3. Connection timeout: Check network connectivity and API endpoint availability
  4. Invalid parameters: Review the tool schemas for required and optional parameters

Logging:

The server logs important events and errors. Check the console output for debugging information.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ambivo_mcp_server_fastmcp-1.0.7.tar.gz (19.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ambivo_mcp_server_fastmcp-1.0.7-py3-none-any.whl (14.8 kB view details)

Uploaded Python 3

File details

Details for the file ambivo_mcp_server_fastmcp-1.0.7.tar.gz.

File metadata

File hashes

Hashes for ambivo_mcp_server_fastmcp-1.0.7.tar.gz
Algorithm Hash digest
SHA256 86203ab307673c3d45338a3dc31d5e0cdbafbe037111f32e5bd6bf86fca648e4
MD5 d65321a2552c76385aa89100ee9e41db
BLAKE2b-256 ecbe9d4c48a1e2c08b2217620af7cad95867f42e483f1f6ab1931428a579906b

See more details on using hashes here.

File details

Details for the file ambivo_mcp_server_fastmcp-1.0.7-py3-none-any.whl.

File metadata

File hashes

Hashes for ambivo_mcp_server_fastmcp-1.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 4b4fb030ce0223e4c6872d8fa486c4dc1b1224daab3c855874d31cca7ea96a88
MD5 e69c05703d2f9a2b4bb408a58e98b3eb
BLAKE2b-256 7bcd2d0552cb5e7619c72ddf70b923c301b1c566cadb6aec82ae8cf5073f5046

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page