"Observability" matching MCP servers:

MCP server for querying observability data from Elasticsearch, SkyWalking, and Prometheus/VictoriaMetrics, enabling AI models to search logs, traces, and metrics across environments.

An MCP server that indexes Claude Code conversation history into SQLite, enabling full-text search across past sessions for context recovery and cross-agent observability.

This project provides a Model Context Protocol (MCP) server that allows language models and other MCP clients to interact with a SuzieQ network observability instance via its REST API.

An MCP server for searching and visualizing SolarWinds Observability logs, allowing users to query log data with filtering options and generate visual representations of log events over time.

Enables AI models to manage a Drupal-shaped content store over MCP, including nodes, vocabularies, terms, tag filtering, and trigram/semantic search, while logging every tool call for observability.

Enables querying private knowledge bases through a modular RAG pipeline with features like hybrid retrieval, reranking, and observability, exposed via the Model Context Protocol.

A modular RAG framework exposing knowledge retrieval tools via MCP, enabling AI assistants to perform hybrid search, reranking, and multimodal document queries with full observability and evaluation.

An open-source Model Context Protocol (MCP) server for the Open Telekom Cloud (OTC) Price Calculator API. Expose OTC pricing data to Claude and other LLM clients with full observability.

Transforms Elasticsearch into an AI-powered observability engine for analyzing logs, APM traces, and system metrics through natural language.

A modular RAG (Retrieval-Augmented Generation) service framework with pluggable architecture and full observability, enabling AI assistants to perform document Q\&A, semantic search, and knowledge base construction through the Model Context Protocol.

A modular RAG framework exposing MCP tools for hybrid document search, enabling AI assistants to query private knowledge bases with multimodal support and full observability.

Enables MCP clients to query a modular RAG knowledge hub with hybrid dense/sparse retrieval, reranking, multimodal image support, and traceable observability, all through natural language tools.

Enables AI clients to retrieve study-abroad admissions knowledge through a local, pluggable RAG pipeline with hybrid search, reranking, citations, and observability. It provides MCP tools, document ingestion, and a Streamlit dashboard for managing and evaluating retrieval.