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qinisolabs

floodwise

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by qinisolabs
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Server Quality Checklist

83%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clear, non-overlapping purposes: one retrieves flood risk data by postcode, the other validates postcode format. No ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun snake_case pattern: 'flood_risk_by_postcode' and 'validate_postcode'.

    Tool Count3/5

    With only 2 tools, the server feels thin for its domain. While the tools are well-defined, the scope is narrow and could benefit from additional flood-related operations.

    Completeness2/5

    The server covers only basic postcode validation and a single flood risk query. Missing features like live flood warnings, property-level risk, or historical data create significant gaps.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 9 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.

    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

  • Behavior4/5

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

    With no annotations, the description fully carries the burden of transparency. It discloses the output (highest risk band, address counts per band, groundwater indication) and the honest 'not found' response. It also highlights limitations. It does not discuss authentication or rate limits, but for a simple query tool, this is adequate.

    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, using a bold 'IMPORTANT' section for key constraints. Every sentence adds value, with no wasted words.

    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 (one parameter, no output schema), the description is complete enough. It explains the output structure, limitations, and error behavior. Lack of detailed output schema is acceptable because the description covers the key return fields.

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

    Parameters4/5

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

    The schema covers the single parameter with example format, but the description adds the critical constraint that it is for England postcodes only, clarifying the geographic scope. This adds meaningful value beyond 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 tool retrieves the Environment Agency's long-term flood-risk indication for an England postcode. It specifies the verb 'get' and the resource, and distinguishes from the sibling 'validate_postcode' by describing its function as returning risk bands rather than validation.

    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 explicitly tells when to use the tool ('USE THIS to get ... instead of guessing') and provides concrete use cases (triaging insurance quotes, underwriting). It also states limitations (England only, area-level not property-level, long-term risk not live warning). However, it does not explicitly mention when not to use or alternatives beyond the sibling.

    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?

    Discloses deterministic format check only, no lookup, and return format. However, does not specify behavior on invalid input (error or response).

    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 concise sentences, imperative tone, no redundancy.

    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?

    Covers purpose, input, behavior, and return structure well for a single-parameter tool, but lacks details on invalid input handling.

    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?

    Adds significant meaning beyond schema: UK postcode, well-formed, splitting, canonical spaced form. Schema just says 'UK postcode to validate'.

    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?

    Clearly states it validates a UK postcode and splits it into outward/inward codes, distinguishing it from the sibling tool which is about flood risk.

    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?

    Explicitly tells when to use ('before storing or matching') and what it doesn't do ('no lookup'), but lacks explicit alternatives or when not to use.

    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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