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Version: 0.7.0

Using MCP tools

Introduced 0.7.0

Model Context Protocol (MCP) is an open protocol standard that provides a standardized way for AI models to connect to external data sources and tools. Lucenia integrates with MCP, enabling agents to use external tools and data sources through MCP servers.

important

This is an experimental feature and is not recommended for use in a production environment. For updates on its progress, see the Lucenia version history.

Overview​

Lucenia 0.7.0 ships an experimental MCP server as part of the ML Commons plugin. The MCP server provides several key capabilities:

  • Server-Sent Events (SSE) Interface: The server publishes a core set of tools as first-class MCP endpoints over a streaming Server-Sent Events (SSE) interface (/_plugins/_ml/mcp/sse).
  • Tool Discovery and Execution: An LLM agent—for example, LangChain's ReAct agent—can simply connect to the server, discover the tools it offers, and then invoke the tools using JSON arguments.

Prerequisites​

The default HTTP transport method does not support streaming. You must install the transport-reactor-netty4 HTTP transport plugin and use it as the default HTTP transport layer.

# Install the transport plugin
./bin/lucenia-plugin install transport-reactor-netty4

# Configure as default transport in lucenia.yml
http.type: reactor-netty4

Two implementation approaches​

Lucenia provides two ways to run an MCP server:

1. Built-in MCP Server (Lucenia 0.7.0+)​

The built-in MCP server is integrated into the ML Commons plugin and accessible via the /_plugins/_ml/mcp/sse endpoint.

Advantages:

  • No additional setup required
  • Direct integration with Lucenia's ML Commons tools
  • Automatic authentication and authorization

Usage:

# Connect to the built-in MCP server
curl -X GET "https://localhost:9200/_plugins/_ml/mcp/sse" \
  -H "Authorization: Basic <credentials>" \
  -H "Accept: text/event-stream"

2. External MCP Client Connection​

The Lucenia MCP client allows Lucenia agents to connect to external tool providers through the MCP protocol, significantly expanding their capabilities beyond native Lucenia functions.

Advantages:

  • Access to specialized external services (weather forecasts, translation APIs, document processing tools)
  • Flexibility to use third-party MCP servers
  • Language-specific integrations

Available MCP APIs​

ML Commons supports the following MCP APIs:

Current limitations​

  • MCP tools can only be used with conversational or plan-execute-reflect agent types
  • This is an experimental feature and not recommended for production use

Configuration​

Authentication​

When connecting to the MCP server, you'll need to include appropriate authentication headers based on your Lucenia security setup:

# Basic authentication
curl -X GET "https://localhost:9200/_plugins/_ml/mcp/sse" \
  -H "Authorization: Basic $(echo -n 'username:password' | base64)" \
  -H "Accept: text/event-stream"

For Python clients​

For Python MCP clients, use this URL to establish the connection:

/_plugins/_ml/mcp/sse?append_to_base_url=true

Next steps​

Future development​

As this experimental feature matures, we anticipate a growing ecosystem of MCP-compatible tools that will make Lucenia agents increasingly capable of executing complex, multi-step tasks.