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DrishtantKaushal

Langfuse MCP Server

by DrishtantKaushal
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Python
Remote

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    Deploy Server
    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

    B3.1/5.0

    Scored across 58 tools

    Disambiguation4/5

    Most tools target distinct resource+action pairs, and descriptions contain helpful cross-references (e.g., fetch_traces vs fetch_trace vs search_trace_content). However, fetch_scores and list_scores_v2 both list scores and differ only by API version, and the error-related tools (find_exceptions, get_error_count, get_exception_details) have somewhat fuzzy boundaries that descriptions must work hard to clarify.

    Naming Consistency3/5

    Nearly all names follow a verb_noun pattern with descriptive nouns, but the retrieval verbs are used inconsistently: get_prompt/get_dataset/get_model vs fetch_trace/fetch_observations/fetch_sessions for the same kind of operation, and list_scores_v2 sits alongside fetch_scores. The mixed get/fetch/list convention creates avoidable confusion.

    Tool Count2/5

    58 tools is far above the comfortable range and will strain agent tool-selection accuracy and context budgets. While Langfuse is a broad platform, this surface spans CRUD, analytics, and management concerns that could be split into separate servers or consolidated (e.g., the 10+ analytics tools repeatedly fetch and aggregate traces in overlapping ways).

    Completeness4/5

    The surface covers the core Langfuse domains well: traces, sessions, observations, scores (read/write), prompts, datasets, comments, models, and annotation queues, plus a robust analytics layer. Minor gaps exist (no delete for comments/prompts/datasets, no model creation), but these align with Langfuse API constraints and don't create dead ends for common workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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