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z9905080

MCP Server for langfuse

by z9905080
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JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap with other tools. The tool's purpose is clear and unambiguous.

    Naming Consistency5/5

    The single tool uses a clear snake_case naming convention. Although there are no other tools to compare, the naming is consistent within itself.

    Tool Count1/5

    A server named 'MCP Server for langfuse' with only one tool is severely undersized. Langfuse is a comprehensive LLM observability platform expecting tools for traces, scores, sessions, etc., not just metrics queries.

    Completeness1/5

    The server lacks any tools for creating, updating, or deleting resources, and does not cover common operations like managing traces or observations. It is severely incomplete for its stated domain.

  • Average 2.7/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations exist, and the description does not disclose any behavioral traits such as side effects, permissions, rate limits, or data scope. It merely describes a query, which implies a read-only operation but does not explicitly confirm safety or other constraints.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely short (3 words) but underspecified for a tool with 8 parameters. It lacks necessary detail and is not front-loaded with critical information. Conciseness should not sacrifice completeness.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity of 8 parameters, no annotations, and no output schema, the description is insufficient. It does not explain what the metrics represent, how results are returned, or any context for interpreting the data. The agent is left without enough information to effectively use the tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, so the baseline is 3. The description adds no meaning beyond the schema's parameter descriptions. It does not explain how to use parameters together or provide context that helps the agent understand parameter relationships.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Description states 'Query LLM metrics' which clearly identifies the verb and resource. It is a simple, direct statement of the tool's purpose, though it lacks specificity about which metrics. It is not a tautology and distinguishes the tool as a query operation.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance provided on when to use this tool, what scenarios it is suited for, or any alternatives. The description gives no context for appropriate usage, leaving the agent without decision-making information.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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