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cognis-digital

repolens

by cognis-digital
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Python

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: pack builds a context pack with a provenance manifest, while verify checks the integrity of that manifest. No overlap or ambiguity.

    Naming Consistency5/5

    Both tool names are imperative verbs (pack, verify), following a simple and consistent pattern. No mixing of styles or conventions.

    Tool Count3/5

    With only 2 tools, the set feels thin for a general-purpose server. While the tools are focused, the count is borderline according to calibration guidelines (1-2 tools is considered thin).

    Completeness5/5

    The server fully covers its stated purpose: building a context pack with a provenance manifest and verifying the manifest's integrity. There are no obvious gaps for this narrow domain.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

  • 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

  • Behavior2/5

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

    No annotations are provided, and the description does not disclose behavioral traits such as what happens on verification failure (e.g., error vs boolean return), side effects, or security/permission requirements. The description carries the full burden but only states the core verification purpose.

    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 a single short sentence that is front-loaded and concise. However, it sacrifices necessary detail for brevity, making it less informative than it could be.

    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 no output schema, no annotations, and only one parameter with 0% schema coverage, the description is insufficient. It does not explain return values, error behavior, or structure of the 'provenance' array, leaving the agent underinformed for correct invocation.

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

    Parameters2/5

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

    Schema coverage is 0%, so the description must compensate. It identifies that 'provenance' is a list of rows, which adds slight meaning beyond the schema's 'type: array', but does not explain the structure of rows or constraints, leaving significant ambiguity.

    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?

    Description clearly states the verb 'Verify' and the resource 'provenance manifest' with the specific purpose of checking it forms an unbroken hash chain, distinguishing itself from the sibling tool 'pack' which likely creates such manifests.

    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 on when to use this tool versus alternatives. The only sibling is 'pack', but no context is given about when verification is appropriate or when packing would be preferred.

    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?

    No annotations are provided, so the description carries full burden. It discloses key behaviors like determinism, token budgeting, AST awareness, and provenance manifest generation. However, it does not state whether the tool is read-only, any authentication needs, rate limits, or side effects, leaving some uncertainty.

    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 a single sentence that efficiently packs many details. It is front-loaded with the main action. While slightly long, every phrase adds value, making it effective without being overly verbose.

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

    Completeness3/5

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

    The description explains the tool's purpose and key behaviors but lacks details about the output structure (no output schema) and does not describe the provenance manifest contents. For a tool with 7 parameters and no annotations, more details on results and usage context would improve completeness.

    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 high (86%), so parameters are mostly documented. The description adds holistic context (e.g., that the pack is token-budgeted gives meaning to 'budget'), but does not detail individual parameters beyond what the schema already provides. Baseline 3 is appropriate.

    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 with a specific verb (Build), resource (context pack), and key characteristics (deterministic, token-budgeted, AST-aware, provenance manifest). It distinguishes from the sibling tool 'verify' by focusing on building a pack.

    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 implies usage for building context packs from repository paths or queries but provides no explicit guidance on when to use this tool versus alternatives, nor any when-not-to-use conditions. The distinction from 'verify' is not addressed.

    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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