Loki MCP Server

An MCP (Model Context Protocol) server for semantic log querying via Loki. Designed to help AI models answer natural language questions about your cluster logs.

Features

• Error Summary: Aggregate errors across your cluster with breakdowns by type
• Pod Restart Detection: Find crashing/restarting pods
• Log Search: Regex-based log search across your cluster
• Namespace/Pod Discovery: List available namespaces and query specific pods
• Semantic Tool Design: Tool names and parameters match natural language questions

Tools

get_error_summary

Get a summary of errors happening in your cluster.

• namespace: Filter to specific namespace (empty = all)
• hours: Look back this many hours (default: 1)

find_pod_restarts

Find pods that have restarted or crashed recently.

• namespace: Filter to specific namespace (empty = all)
• hours: Look back this many hours (default: 1)

search_logs

Search logs with a regex pattern.

• query: Regex pattern to search for
• namespace: Filter to specific namespace (empty = all)
• hours: Look back this many hours (default: 1)
• limit: Maximum log lines to return (default: 100)

list_namespaces

List all namespaces that have logs in Loki.

get_pod_logs

Get logs for a specific pod.

• pod_name: Pod name (supports wildcards like ollama*)
• namespace: Namespace of the pod (empty = search all)
• hours: Look back this many hours (default: 1)
• limit: Maximum log lines to return (default: 100)

Development

# Install dependencies
uv sync

# Run the server
uv run server.py

The server exposes FastMCP over streamable HTTP at /mcp (default port 8000).

Deployment (In-Cluster)

Add to your local-k8s-apps Helm values with:

• Deployment with the MCP server
• Exposed via HTTP on port 8000
• Environment variable: LOKI_URL pointing to Loki service

The Ollama MCP bridge will load this server's tools and expose them to the model.