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smkbarbosa

guia-wcag-mcp

by smkbarbosa
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: retrieving details for a known criterion, searching by keyword, and generating a manual test routine. There is no meaningful overlap between them.

    Naming Consistency5/5

    All tool names follow the same lowercase snake_case verb_noun pattern: get_criterion_details, search_criteria_by_keyword, generate_manual_test_routine. The naming is predictable and consistent.

    Tool Count5/5

    With only three tools, the server is compact but well-scoped for its niche purpose. Each tool earns its place by covering a distinct aspect of working with WCAG criteria.

    Completeness4/5

    The server covers the core workflow: finding criteria, retrieving details, and generating manual test routines. A minor gap is the lack of a way to browse or list all criteria at once, but agents can work around this with keyword search.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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  • 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?

    No annotations are provided, so the description must carry the full behavioral burden. 'Filtra' (filters) conveys a read-only, non-destructive operation and indicates the outcome (matching cards), but it stops short of stating matching semantics (e.g., substring vs. exact, case sensitivity) or whether any side effects occur.

    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 a single, front-loaded sentence that states the operation, the resource, and an example without any filler. It is appropriately sized for a trivial one-parameter search.

    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?

    For a simple search tool with one parameter and no output schema, the description adequately communicates the returned resource (matching guide cards). It does not detail the return shape or matching behavior, but given the tool's low complexity this is acceptable.

    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?

    The input schema already documents the keyword parameter with 100% coverage, including an example. The tool description repeats the same example without adding new meaning, so it meets the baseline but adds no extra semantic value.

    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 uses the specific verb 'filtra' (filters) with the resource 'cartões do guia' and makes the search criterion explicit ('palavra-chave'). This clearly conveys what the tool does, though it does not name the sibling tools or explicitly contrast with them.

    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 this tool is for finding guide cards based on a keyword, but it gives no explicit guidance on when to choose it over get_criterion_details or generate_manual_test_routine. The intended context is inferable from the examples, but exclusions and alternative routing are absent.

    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 are provided, so the description carries the behavioral burden. It discloses that the routine is step-by-step, covers keyboard and screen reader, and importantly states that execution and verdict remain human, preventing misuse as an automated testing tool. It could add more about output structure, but the core behavioral constraint is clearly conveyed.

    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 two short sentences with no filler. It front-loads the main output and then adds the crucial human-in-the-loop caveat, making every sentence valuable.

    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?

    For a simple one-parameter tool with no output schema, the description adequately defines the deliverable and its scope. It could further describe the expected structure of the routine or behavior for invalid criteria, but the essential information needed to call the tool correctly is present.

    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 coverage is 100%, and the single parameter criterion is already documented with an example. The description confirms the criterion is a WCAG criterion but does not add significant additional meaning 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 uses a specific verb and resource: it generates a step-by-step manual test routine for a WCAG criterion, with explicit mention of keyboard and screen reader coverage. This clearly distinguishes it from siblings like get_criterion_details and search_criteria_by_keyword, since only this tool produces a test routine.

    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 when to use the tool: when a manual WCAG test routine is needed. However, it does not explicitly state when not to use it or mention alternatives such as get_criterion_details for criterion information, so usage guidance is left to inference.

    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?

    With no annotations, the description carries the burden of disclosing behavior. 'Retorna' clearly signals a read-only retrieval with no side effects, and the response content is described. It does not cover edge cases like invalid criterion numbers or exact-match behavior, but for a simple lookup 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 a single focused sentence with no filler. The core resource, the returned content, and an example are all included efficiently.

    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?

    For a tool with one required parameter, no output schema, and no nested objects, the description gives enough information for an agent to call it correctly. The only minor gap is the lack of explicit output structure, but the described return content makes the call's purpose and result clear.

    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?

    The schema already fully describes the criterion parameter with an example, and the description repeats the WCAG criterion concept without adding new semantic details. At 100% schema description coverage, the baseline score of 3 applies.

    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 returns the simplified guide description and ABNT NBR 17225 normative items correlated to a WCAG success criterion. The verb 'Retorna' and the resource are specific, and the example format "1.4.3" disambiguates from the sibling keyword-search tool.

    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 intended use is inferred: call it when you have a specific WCAG criterion number and need its correlated NBR details. However, the description does not explicitly state when to prefer this over search_criteria_by_keyword or generate_manual_test_routine, nor does it mention any exclusions or prerequisites.

    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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