Lambda MCP

A Python MCP (Model Context Protocol) server for querying Elasticsearch via Kibana proxy.

Description

Lambda MCP is a FastMCP-based server that provides tools for querying Elasticsearch clusters through Kibana's proxy API. It supports authentication and includes token counting to manage response sizes.

Features

• Query Elasticsearch via Kibana proxy
• Support for Basic Authentication
• Token counting with configurable limits
• JSON query validation
• Error handling for network and authentication issues

Installation

From source

1. Clone the repository:

git clone https://github.com/yourusername/lambda-mcp.git
cd lambda-mcp

2. Install the package:

pip install -e .

Using pip

pip install lambda-mcp

Usage

As a standalone script

python lambda_mcp.py

Using the installed command

lambda-mcp

Environment Variables

• LAMBDA_MCP_MAX_TOKEN_NUM: Maximum number of tokens allowed in response (default: 10000)

API

query_elasticsearch_via_kibana

Query Elasticsearch via Kibana proxy.

Parameters:

• base_url (str): Kibana base URL (e.g., https://kibana.example.com)
• username (str): Username for Kibana authentication (use empty string if no auth required)
• password (str): Password for Kibana authentication (use empty string if no auth required)
• path (str): Elasticsearch query path (e.g., index/_search)
• query (str, optional): JSON query body as string (default: "{}")

Returns:

• str: The query result as a JSON string

Example:

result = query_elasticsearch_via_kibana(
    base_url="http://kibana.example.com",
    username="your_username",
    password="your_password",
    path="_cat/indices",
    query="{}"
)

Development

Install development dependencies

pip install -e ".[dev]"

Code formatting

black lambda_mcp.py
isort lambda_mcp.py

Type checking

mypy lambda_mcp.py

Testing

pytest

Dependencies

• requests>=2.25.0: HTTP library for API calls
• fastmcp>=0.1.0: FastMCP framework for MCP servers
• tokenizers>=0.13.0: Token counting functionality

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.