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goww7

ActTrace

by goww7
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Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one classifies risk categories, the other generates transparency notices. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tools use the 'acttrace_' prefix and snake_case. However, one uses a single verb 'classify' while the other uses a compound verb 'generate_transparency_notice', introducing minor inconsistency.

    Tool Count3/5

    With only 2 tools, the surface is minimal. While the domain is narrow, the count is borderline thin and may require more tools for a complete compliance workflow.

    Completeness3/5

    The tools cover classification and transparency notice generation, which are core needs. Missing are tools for reclassification, updating features, or more detailed obligation queries, leaving minor gaps.

  • Average 4.7/5 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 Apache 2.0.

  • 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It explains the output (user-facing copy, placement, caveats, human-review recommendation) and notes the notice is a draft, not legal advice. However, it does not explicitly state that the tool is read-only or describe any side effects, permissions needed, or rate limits, leaving some transparency 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 concise and well-structured: it starts with the main purpose, then outlines output details, usage guidance, and parameter descriptions in a logical order. Every sentence adds value without redundancy, making it easy to scan for key information.

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

    Completeness5/5

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

    Given 7 parameters, 2 required, and an output schema, the description covers all aspects: input details, output content, usage context (domain, non-legal status), and constraints (language limitation for MVP). It provides enough information for an agent to invoke the tool correctly without relying on external documentation.

    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?

    Schema description coverage is 0%, so the description must compensate. It fully describes each parameter, including allowed values (e.g., notice_type options), defaults (tone, language), and optionality (feature_name, human_review_level, risk_category). This adds significant meaning beyond the raw schema, enabling correct parameter selection.

    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's purpose: generating an EU AI Act Article 50 transparency notice. It specifies the verb 'Generate' and the resource 'transparency notice', and distinguishes from the sibling tool 'acttrace_classify' by detailing the output content such as user-facing copy and placement.

    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 provides explicit usage examples ('AI transparency notice', 'Article 50 notice') and clarifies the tool's domain (non-financial SaaS/technology products) and limitations (not legal advice). It lacks explicit exclusions or alternative tool recommendations, but the context is clear enough for appropriate selection.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses behavior: runs deterministic classification engine, returns detailed fields, states limitations (empty description yields unknown), and clarifies the tool is informational, not legal advice. No hidden side effects.

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

    Conciseness4/5

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

    The description is front-loaded with the essential purpose and then provides structured details. It is somewhat lengthy but well-organized, with each sentence serving a purpose. Could be slightly trimmed, but overall efficient given the complexity.

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

    Completeness5/5

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

    Given the tool's complexity (12 parameters, regulatory sensitivity) and the presence of an output schema, the description is exceptionally complete. It covers purpose, usage, parameter details, output fields, scope restrictions, and a legal disclaimer. No gaps remain.

    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?

    Schema description coverage is 0%, so the description carries the full burden. The 'Args:' section provides thorough explanations for all 12 parameters, including allowed values for human_review_level and guidance on when fields are required (e.g., model_provider for user-facing features). This adds critical meaning beyond the bare 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's purpose: 'Classify an AI feature's EU AI Act risk category.' It lists possible risk categories and specifies the scope (non-financial SaaS/technology products), distinguishing it from the sibling tool for generating transparency notices.

    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 tells when to use: 'Use this when asked "Is this AI feature EU AI Act compliant?", "What risk tier does my AI feature fall into?", or for a general AI Act risk classification.' Also explains when not to use (financial services without override) and notes the result is not legal advice.

    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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