Jaeger MCP Server

The Jaeger MCP Server bridges the gap between distributed tracing systems and AI‑powered development tools. By exposing Jaeger’s rich telemetry data through the Model Context Protocol, it lets AI assistants query, filter, and analyze traces directly from within IDEs, chat interfaces, or custom workflows. This capability is especially valuable when developers need to understand latency bottlenecks, debug service interactions, or surface performance insights without leaving their coding environment.

At its core, the server connects to a Jaeger instance via either HTTP or gRPC, configurable through environment variables such as , , and . Once connected, it offers a set of intuitive tools that map common tracing operations to simple function calls. Developers can retrieve lists of services, enumerate available operations per service, search for traces that match specific criteria, or pull a complete trace by its ID—all through declarative JSON payloads. This eliminates the need for manual API calls or custom scripts, allowing AI assistants to surface trace data as part of natural language conversations or code suggestions.

Key features include:

• Service and operation discovery: and provide quick introspection of the Jaeger ecosystem, enabling AI assistants to suggest relevant services or endpoints during debugging sessions.
• Trace retrieval: fetches the full span hierarchy for a given trace ID, supporting optional time windows to narrow results.
• Trace search: allows complex queries based on service name, operation, and arbitrary span attributes, making it possible to locate problematic traces that match user‑defined filters.
• Secure integration: Optional lets the server authenticate against protected Jaeger deployments, ensuring that sensitive telemetry remains secure.

In practice, this MCP server is a powerful asset for several real‑world scenarios:

1. Real‑time debugging – A developer asks the AI assistant, “What’s causing the latency spike on ?” The assistant can invoke , filter by operation and time, and return the most relevant trace for inspection.
2. Performance reviews – During a code review, an AI assistant can automatically surface recent traces for newly added endpoints, helping reviewers spot regressions or inefficient patterns.
3. Observability onboarding – New team members can ask the assistant to list all services and their operations, receiving a structured overview without consulting external dashboards.
4. Automated alerts – CI pipelines can query the MCP server to verify that critical traces exist after a deployment, ensuring end‑to‑end flow integrity.

By integrating seamlessly with popular MCP clients—such as VS Code, Claude Desktop, or Cursor—the Jaeger MCP Server turns distributed tracing data into an interactive, AI‑driven knowledge base. Its straightforward tool set and secure configuration make it a standout solution for developers who need instant, contextual insight into their microservice architectures.