OpenTelemetry is an observability framework for cloud-native software. It provides tools, APIs, and SDKs to capture telemetry data from applications and process it for understanding their behavior and performance.
Self-hosted semantic code search for AI coding agents. Indexes many repositories at once with tree-sitter AST chunking, local llama.cpp embeddings and Qdrant, so Claude Code, Cursor, Codex and any MCP client can find code by meaning, trace usages and callers, and read the git history behind each chunk. No API keys, and your code never leaves your machine.
2
18
35
MIT
Why this server?
Forwards analytics and call telemetry to OpenTelemetry-compatible observability backends.
Ask your AI agent about your MCP servers: which tools fail, how a release compares, what clients look for and do not find. Reads from mcpspan with a read token.
10
11
21
MIT
Why this server?
Enables access and analysis of OpenTelemetry traces and metrics data stored in Logfire, with tools for finding exceptions, retrieving trace information, and executing SQL queries against telemetry data.
A Model Context Protocol server that enables LLMs to retrieve and analyze OpenTelemetry traces and metrics from Logfire, supporting exception tracking and custom SQL queries against telemetry data.
Uptime monitoring from your AI agent: create and diagnose checks (40 types: HTTP, TCP, DNS, SSL, ICMP, databases, message queues and more), read results and incidents, and manage status pages and maintenance windows. Built into the self-hostable SolidPing server, served over streamable HTTP at /api/v1/mcp.
23
28
AGPL 3.0
Why this server?
Provides read-only MCP tools for querying OpenTelemetry telemetry in the dashboard, including logs, traces, metrics, service lists, individual trace details, and metric points.
Query OpenTelemetry logs, traces and metrics from a self-hosted OTLP dashboard.
8
17
GPL 3.0
Why this server?
Provides performance monitoring and metrics collection through an OpenTelemetry-compatible system for tracking tool utilization and provider performance.
An MCP server that enables multi-provider AI collaboration using models like DeepSeek, OpenAI, and Anthropic through strategies such as parallel execution and consensus building. It provides specialized tools for side-by-side content comparison, quality review, and iterative refinement across different AI providers.
4
1
MIT
Why this server?
Provides mock tools that can simulate interactions with a Kubernetes cluster running OpenTelemetry Demo, useful for testing agents against realistic but static data without requiring a live cluster.
Provides a set of read-only Kubernetes functions via an MCP server, enabling interaction with Kubernetes clusters through agents or coding assistants like GitHub Copilot.
21
700 PyPI
3
Apache 2.0
Why this server?
Provides distributed tracing and metrics collection for MCP server ecosystems, enabling monitoring of requests across multiple services with context propagation and structured performance data collection.
Provides comprehensive monitoring and observability for MCP server ecosystems with real-time health checks, performance metrics, distributed tracing, anomaly detection, and automated performance reports using OpenTelemetry and Prometheus.
22
1
MIT
Why this server?
Provides tools to access and search OpenTelemetry documentation, allowing users to fetch documentation pages in markdown format and search for specific content about tracing, metrics, instrumentation, and SDK configuration.
Enables access to OpenTelemetry documentation by fetching and converting pages to markdown format and searching content using Google Custom Search. Provides comprehensive documentation lookup and search capabilities for OpenTelemetry concepts, instrumentation, and configuration.
