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KarandeepSinghSodhi

Langfuse Trace Fetcher

by KarandeepSinghSodhi
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.3

  • Disambiguation5/5

    Each tool serves a distinct purpose: fetching a filtered list of traces, retrieving full detail for a specific trace, and listing available filter fields. There is no functional overlap.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (fetch_langfuse_traces, get_langfuse_trace_detail, list_langfuse_trace_filters) with snake_case, making the naming predictable and clear.

    Tool Count5/5

    With 3 tools, the set is well-scoped for a trace fetching server: listing with filters, getting detail, and a reference tool for filters. No extraneous or missing tools.

    Completeness5/5

    For a 'Trace Fetcher', the tools cover the core workflow: querying traces with filters, retrieving individual details, and discovering available filters. No essential operations are missing.

  • Average 4/5 across 3 of 3 tools scored. Lowest: 3.4/5.

    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 is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior3/5

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

    With no annotations provided, the description carries full burden. It states that the tool 'connects...using provided credentials' and 'returns...matching traces formatted as readable context', implying a read operation. However, it does not disclose details like rate limits, error behaviors, or any side effects, leaving gaps.

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

    Conciseness5/5

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

    The description is only two sentences, free of redundancy, and every sentence adds value. It is concise and front-loaded with the key purpose.

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

    Completeness4/5

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

    Given the 14 parameters with full schema descriptions and the presence of an output schema, the description is reasonably complete. It could mention pagination or filtering behavior, but the schema and output schema fill in many gaps.

    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 extra meaning beyond 'filtered list', and all parameter details are already in the schema. The description does not enrich parameter understanding.

    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?

    The description clearly states 'Fetch a filtered list of traces from a Langfuse instance', specifying the verb and resource. While it distinguishes from sibling tools by name, it does not explicitly contrast with 'get_langfuse_trace_detail' or 'list_langfuse_trace_filters'.

    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?

    The description provides no guidance on when to use this tool versus alternatives such as 'get_langfuse_trace_detail' (for a single trace) or 'list_langfuse_trace_filters' (to list available filters). There are no when-not-to-use or prerequisite instructions.

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

  • Behavior3/5

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

    With no annotations, the description carries full burden. It describes the return payload but does not disclose behavioral traits like idempotency, rate limits, or error conditions. Adding notes on safety (read-only) and required auth would improve transparency.

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

    Conciseness5/5

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

    Two sentences purpose-first, no filler. Efficiently communicates purpose and return content. Every sentence earns its place.

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

    Completeness4/5

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

    Given the tool's simplicity, an output schema, and 100% parameter coverage, the description is nearly complete. It lacks only minor usage context (e.g., idempotency note), but overall covers the essentials.

    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 description adds no parameter meaning beyond the schema. The baseline of 3 applies; the description mentions 'by its ID' but trace_id is already well-documented in the schema.

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

    Purpose5/5

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

    The description clearly states the verb 'Fetch', resource 'full detail for a single Langfuse trace', and the method 'by its ID'. It lists returned components (input/output, observations, scores, metadata), distinguishing it from siblings that list traces or filters.

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

    Usage Guidelines4/5

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

    The description implies use when needing full detail of one trace, and the contrast with siblings (fetch_langfuse_traces, list_langfuse_trace_filters) is clear. However, it does not explicitly state when not to use or mention alternatives, which would earn a 5.

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

  • Behavior4/5

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

    No annotations provided, but description is transparent: it is a reference tool with no side effects, returning a formatted table. Could mention if results are static or computed, but sufficient for context.

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

    Conciseness5/5

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

    Four sentences, front-loaded with purpose, no redundancy. Every sentence adds value.

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

    Completeness4/5

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

    Given no parameters and existence of output schema, description is nearly complete. Could briefly mention output format or that it's fast, but not necessary.

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

    Parameters5/5

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

    No parameters, schema coverage 100%. Description adds meaning by explaining the output serves as parameter names for fetch_langfuse_traces, going beyond schema.

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

    Purpose5/5

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

    The description clearly states the tool lists available filter fields for fetching traces. It specifies it is a help/reference tool that does not make API calls, distinguishing it from sibling tools like fetch_langfuse_traces.

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

    Usage Guidelines5/5

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

    Explicitly states it does not make API calls and instructs to use the filter names as parameters for fetch_langfuse_traces, providing clear when-to-use and alternative guidance.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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