Web Analysis MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct as it is the only one available.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'web_search' follows a clear verb_noun pattern.
Tool Count2/5A single tool for a server named 'Web Analysis MCP' feels thin and under-scoped. The tool performs web search and summarization, but the server name suggests broader analysis capabilities (e.g., link analysis, content extraction, performance metrics) that are not covered.
Completeness2/5The server's domain appears to be web analysis, but with only a search and summarization tool, there are significant gaps. Missing operations might include analyzing page structure, extracting specific data, checking SEO metrics, or monitoring web changes, making the surface severely incomplete for the implied purpose.
Average 3.1/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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, so the description carries the full burden. It mentions the multi-step process (search, crawl, LLM summarization) and hints at recency ('最新网络信息'), but fails to disclose critical behavioral traits such as rate limits, authentication needs, potential costs, error handling, or what 'LLM 总结' entails. For a complex tool with no annotations, this leaves significant gaps.
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 concise (two sentences) and front-loaded with the core functionality. Every sentence contributes: the first explains what the tool does, and the second provides usage context. There's no wasted text, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-step process with 7 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return format, error conditions, or behavioral constraints. The agent would lack sufficient context to use this tool effectively without trial and error.
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 fully documents all 7 parameters. The description adds no parameter-specific information beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.
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: '使用 SearXNG 搜索网络内容,通过 Creeper 爬取网页,并返回经过 LLM 总结的结果' (search web content using SearXNG, crawl pages via Creeper, and return LLM-summarized results). It specifies the verb (search/crawl/summarize) and resource (web content), but since there are no sibling tools, it cannot demonstrate differentiation from alternatives.
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 provides some usage context: '适用于需要获取最新网络信息的场景' (suitable for scenarios requiring up-to-date web information). This implies when to use it (for current information), but lacks explicit guidance on when not to use it or comparisons to alternatives. No prerequisites or exclusions are mentioned.
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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