lgtm-mcp 0.33.0

LGTM Observability and Alertmanager MCP Server for Agentic AI!

Lgtm MCP

LGTM Stack (Alertmanager and Grafana) system observability orchestrator. Built with the highest architectural standards, incorporating dynamic facades, custom API routing, and FastMCP tool decoration.

Documentation — Installation, deployment, usage across the API and MCP interfaces, and guidance for provisioning the LGTM observability stack are maintained in the official documentation.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Configuration
• MCP Tools
• Architecture
• Deployment
• Documentation
• Contributing
• License

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Overview

Lgtm MCP provides a high-performance, model-optimized interface to Lgtm capabilities. It isolates the model from underlying API transport complexity, ensuring safe, idempotent, and highly traceable system interactions.

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Features

• Dynamic Facade Orchestration: Integrates multi-inheritance clients cleanly under a single facade.
• Battle-Tested Resilience: Out-of-the-box credential authentication, connection polling, and request retry strategies.
• FastMCP Declarative Tools: Fast, native schema registration with full inline validation.
• Complete Test Intent Diversity: Deep, automated unit, integration, and mock tests ensuring high code coverage.

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⚙️ Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

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Installation

Install in editable mode directly inside your active workspace:

pip install -e .[all]

Or via the uv tool:

uv pip install -e .

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Usage

You can launch the FastMCP server in stdio mode via Python module execution:

import asyncio
from lgtm_mcp.mcp_server import get_mcp_instance

async def main():
    mcp = get_mcp_instance()
    # Execute stdio loop or launch server
    print("MCP Server ready.")

if __name__ == "__main__":
    asyncio.run(main())

For direct shell launch, execute:

python -m lgtm_mcp.mcp_server

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Configuration

The package is fully configurable via the environment variables listed below:

Variable	Description	Default	Required
ALERTMANAGER_URL	Prometheus Alertmanager server API URL	http://localhost:9093	Yes
GRAFANA_URL	Grafana server API endpoint	http://localhost:3000	Yes
LGTM_TOKEN	Grafana admin API Key or Service Token	your_grafana_api_token	Yes

A local template is supplied inside .env.example. Copy this file as .env and fill out your specific service endpoint parameters before starting execution.

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MCP Tools

The following declarative FastMCP tools are registered and available to upstream AI agents:

Tool Name	Description	Parameters
get_alerts	Retrieve list of firing alerts from Alertmanager	None
create_silence	Create a new alert silence	matchers: list, duration_hours: int = 2
get_dashboards	Retrieve list of Grafana dashboards	None

See docs/overview.md or docs/concepts.md for deeper operational examples.

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Architecture

This package uses the standardized Agent-Utilities dynamic facade architecture:

graph TD
    User([User Agent]) --> Server[FastMCP Server]
    Server --> Facade[Api Dynamic Facade]
    Facade --> ClientBase[ApiClientBase]
    Facade --> Auth[Credentials Auth Handler]
    ClientBase --> Service([External Service API])

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Deployment

Bare-Metal (Standard pip)

1. Set up your Python virtual environment (>= 3.10).
2. Install the package: pip install .[all]
3. Export credentials:

   export LGTM_URL="http://localhost:3000"
   

4. Run: python -m lgtm_mcp.mcp_server

Container (Docker Compose)

A standard compose structure is provided inside the docker/ folder. Build and deploy:

docker compose -f docker/compose.yml up --build -d

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Additional Deployment Options

lgtm-mcp can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

• Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
• Remote URL — connect to a server deployed behind Caddy at http://lgtm-mcp.arpa/mcp using the "url" key.

Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

Page	Contents
Installation	pip, source, extras, prebuilt Docker image
Deployment	run the MCP server, the agent server, Compose, Caddy + Technitium, env config
Usage	the MCP tools and the Api Python client
Backing Platform	deploy the LGTM observability stack with Docker
Overview	architecture and the dynamic facade
Concepts	concept registry (CONCEPT:LGTM-*)

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Contributing

Please audit all code changes against ecosystem guidelines in CONTRIBUTING.md if available, and run:

pre-commit run --all-files

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License

This project is licensed under the MIT License. See the LICENSE file for complete details.