mcp-web-audit
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'auditPackage' has a clearly defined and distinct purpose focused on auditing dependencies.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'auditPackage' follows a clear verb_noun pattern, which would be consistent if more tools were added.
Tool Count2/5A single tool is too few for a server named 'mcp-web-audit', which suggests a broader scope for web auditing. The tool only covers dependency auditing, leaving gaps for other potential web audit functions like performance checks or accessibility testing.
Completeness2/5The tool set is severely incomplete for the inferred domain of web auditing. It only provides dependency auditing, missing obvious operations such as security scanning, performance analysis, or compliance checks, which are typical in web audit workflows.
Average 3.4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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 ISC License.
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 carries the full burden. It discloses that the audit result is '标准格式的markdown字符串' (standard format markdown string) and should be used directly for display without modification, adding useful behavioral context. However, it doesn't cover other critical aspects like required permissions, rate limits, error handling, or whether the operation is read-only or mutative. The description adds some value but leaves significant gaps for a tool performing security audits.
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 appropriately concise with two sentences that efficiently cover purpose, scope, and output usage. It's front-loaded with the core function, and each sentence adds value without redundancy. A minor deduction for slightly awkward phrasing in the second sentence, but overall it's well-structured and waste-free.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (security auditing with 2 parameters) and no annotations or output schema, the description is moderately complete. It explains what the tool does, the types of inputs (local/remote), and output format, but lacks details on behavioral traits like safety, permissions, or error handling. Without an output schema, it should ideally describe return values more thoroughly, but the markdown format note provides some compensation. It's adequate but has clear gaps.
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%, with both parameters well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'projectRoot' or 'savePath' further). According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '审计前端工程的所有直接和间接依赖,得到安全审计结果' (audits all direct and indirect dependencies of a frontend project to obtain security audit results). It specifies the verb ('审计' - audit) and resource ('前端工程' - frontend project) with scope ('所有直接和间接依赖' - all direct and indirect dependencies). However, with no sibling tools provided, it cannot demonstrate differentiation from alternatives, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating '支持本地工程的审计,也支持远程仓库的审计' (supports auditing local projects and remote repositories), which suggests when to use it based on project location. However, it lacks explicit guidance on when not to use it or alternatives, and no prerequisites are mentioned. With no sibling tools, comparative guidance isn't applicable, but the implicit context is sufficient for a baseline score.
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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