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.
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.
Last updated
4
161
MIT
Why this server?
The instrumented agents emit OpenTelemetry-compatible traces, metrics, and logs for observability.
An MCP server that automatically instruments Python AI agents with the ioa-observe-sdk, adding OpenTelemetry-based tracing, metrics, and logs with zero manual effort.
Last updated
2
Apache 2.0
Why this server?
Supports including OpenTelemetry as a component in architecture diagrams.
Generates Excalidraw architecture diagrams with support for 60+ components including GCP, Kafka, and AI/Agentic shapes. Provides MCP tools for creating, modifying, and converting diagrams from structured input or Mermaid syntax.
Last updated
4
1
MIT
Why this server?
Provides distributed tracing capabilities through OpenTelemetry middleware, enabling observability and performance monitoring of MCP server operations.
About
The fast, idiomatic way to build MCP servers in Go. Gin-like DX with struct-tag auto schema, middleware, adapters for Gin/OpenAPI/gRPC.
Last updated
5
13
Apache 2.0
Why this server?
Core integration that emits OpenTelemetry traces, metrics, and logs to OTLP endpoints, supporting all three OTLP wire formats (gRPC, HTTP/protobuf, HTTP/JSON) for comprehensive observability data collection.
An MCP server that emits OpenTelemetry traces, metrics, and logs to OTLP endpoints in various formats. It includes tool-mimicry profiles to generate realistic traffic resembling common tools like nginx, postgres, and AWS Lambda for testing and demonstration purposes.
Last updated
11
MIT
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.
Last updated
4
1
MIT
Why this server?
Supplies documentation and configuration standards for integrating OpenTelemetry observability within agent architectures.
Provides standards documentation and validation tools for the Darwin platform, including naming conventions and agent card validation. It enables AI agents to search platform standards and access resource templates for design and implementation.
Last updated
6
Why this server?
Provides access to OpenTelemetry repositories, documentation, examples, semantic conventions, and instrumentation scoring to help implement high-quality observability.
Bridges AI coding assistants with the OpenTelemetry ecosystem, providing real-time access to repositories, documentation, examples, semantic conventions, and instrumentation scoring for high-quality observability.
Last updated
9
19
Apache 2.0
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.
