superaudit-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Only one tool exists, so there is no ambiguity between tools. The single tool has a clear, distinct purpose around website auditing.
Naming Consistency5/5With a single tool, the naming is trivially consistent. 'audit_website' follows a clear verb_noun pattern.
Tool Count3/5A single tool feels thin and borderline for a server. It consolidates many audit checks into one call, but offers no auxiliary operations like listing or retrieving past audits.
Completeness4/5The tool comprehensively covers the core audit workflow, including many modules. Minor gaps exist, such as no ability to fetch historical audits or compare results over time.
Average 4.2/5 across 1 of 1 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.
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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 carries the full burden, and it discloses important behavior: it is a real and free audit, it respects a fair-use rate limit on SuperAudit's server, and it returns a global score, per-module detail, and prioritized problems. It does not state explicit side-effect/safety information, but an audit is clearly presented as a non-mutating analysis, and the rate-limit caveat is a useful limitation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but not bloated: the first sentence delivers the core purpose and scope, followed by the output format, use cases, and rate limit. Information is front-loaded and every clause earns its place, though the first sentence is long.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema or annotations, the description covers what the tool does, what it returns, when to use it, and an operational constraint (rate limit). Combined with a fully documented input schema, an agent has enough context to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents 'url' and 'raw_json'. The description itself does not add parameter-specific detail; it only contextualizes the overall output. This meets the baseline but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and object: 'Ejecuta una auditoría real y gratuita de SuperAudit sobre una web', and enumerates the audit areas (SEO técnico, seguridad, RGPD/LSSI, Core Web Vitals, accesibilidad, GEO, WordPress/CVEs). It also states the concrete return value (score 0-100, module details, prioritized issues). With no sibling tools to disambiguate, the purpose is fully identifiable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives concrete use cases: '¿qué falla en la web de mi cliente?' or 'audita esta URL antes de contactarles'. This makes the intended invocation context clear. There are no explicit exclusions or alternative tool routing, but no siblings are provided, so this is appropriate.
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 the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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