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nik-kale

pangram-editorial

by nik-kale
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Both tools analyze AI attribution but serve different purposes: one provides a comprehensive audit with segment-level breakdowns, the other gives a quick summary. Their descriptions clearly distinguish them, so no ambiguity.

    Naming Consistency4/5

    Both tools use the 'pangram_' prefix and snake_case, but one uses 'attribution_audit' (noun_noun) and the other 'quick_snapshot' (adjective_noun). This is a minor inconsistency; otherwise, naming is predictable.

    Tool Count3/5

    The server has only 2 tools, which feels thin for a domain that could benefit from additional tools like batch analysis or report generation. However, the two levels of detail (quick and comprehensive) are well-scoped for its purpose.

    Completeness4/5

    For the narrow domain of AI attribution analysis, the tools cover quick and detailed analysis. There are no obvious missing operations, as it's a pure analysis service. Minor gap: no batch or comparison feature.

  • Average 3.9/5 across 2 of 2 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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?

    Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds context about requiring an API key, minimum text length, and analysis details, but does not disclose potential limitations or error handling, which would enhance transparency.

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

    Conciseness3/5

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

    The description is structured with bullet points and sections, but is somewhat verbose and could be more concise. Some information is redundant with the schema, and the description could be trimmed without losing clarity.

    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 has two parameters, no output schema, and annotations, the description covers purpose, parameters, output formats, and use cases adequately. It mentions required environment variable and minimum text length. Could be improved by noting error conditions or rate limits, but overall complete.

    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%, with both parameters described in the schema. The description reiterates this information and adds a default for response_format, but does not provide meaningful additional semantics beyond what the schema already offers.

    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 the tool analyzes text for AI attribution using Pangram's API, listing specific outputs and use cases. However, it does not differentiate from the sibling tool 'pangram_quick_snapshot', missing the opportunity to clarify unique purpose.

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

    Usage Guidelines3/5

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

    The description provides several use cases (editorial review, transparency audits, QA, compliance) but does not specify when not to use this tool or suggest alternatives. Given the sibling tool, explicit guidelines for selection would be beneficial.

    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?

    Annotations already indicate read-only and idempotent. Description adds that it's a snapshot, returns specific metrics, and has word count requirement. No contradictions.

    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?

    Description is brief, well-structured with args, returns, and example. No superfluous content.

    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?

    Tool is simple but description covers purpose, usage context, parameter requirement, and return structure with example. Lacks explicit error handling or edge cases, but adequate for this 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 description covers the parameter; description repeats the 50-word requirement but adds no new semantic information beyond the schema. Baseline 3 due to high schema coverage.

    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?

    Explicitly states 'get a quick attribution snapshot for editorial review' and contrasts with sibling tool by noting it's for rapid checks not detailed analysis. Distinguishable.

    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?

    Recommends using for rapid checks when detailed segment analysis isn't needed, and states text must be at least 50 words.

    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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