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Langfuse MCP Server

Langfuse MCP Server

by therealsachin
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    • langfuse-mcp-java

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    • Langfuse MCP Server

      Catherineh0509
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      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents and developers to query Langfuse telemetry locally, fetching traces, sessions, observations, and exceptions, inspecting route decisions, and running metrics, prompt, dataset, and annotation-queue operations. Supports selective tool-group loading and a read-only mode for token-efficient debugging from Claude Code, Codex, Cursor, or any MCP client.
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    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.2/5.0

    Scored across 24 tools

    Disambiguation4/5

    Most tools have distinct purposes targeting specific resources (e.g., get_comment vs. get_metrics), but some overlap exists between get_observations and get_trace_detail (since traces include observations) and between get_daily_metrics and project_overview (both provide aggregated metrics). Descriptions help clarify, but agents might occasionally misselect between these related tools.

    Naming Consistency5/5

    Tool names follow a highly consistent verb_noun pattern throughout, with 'get_' for retrieving specific items, 'list_' for listing collections, and descriptive nouns (e.g., get_trace_detail, list_datasets). There are no deviations in naming conventions, making the set predictable and readable.

    Tool Count3/5

    With 24 tools, the count is borderline high for a single server, potentially overwhelming for agents. While the Langfuse domain is broad (covering traces, metrics, datasets, etc.), the tool set feels heavy and could benefit from consolidation or better scoping to reduce cognitive load.

    Completeness5/5

    The tool surface provides comprehensive coverage for the Langfuse domain, including CRUD-like operations (get/list for traces, observations, datasets, prompts, models, comments), detailed metrics (cost, usage, health), and specialized analyses (top_expensive_traces, usage_by_model). No obvious gaps exist; agents can perform full lifecycle and analytical workflows.

    Resources