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manooll

WebFetch.MCP

by manooll
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.8

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches the web, the other fetches a specific page. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern with snake_case: web_search and web_fetch. The naming is perfectly consistent.

    Tool Count4/5

    With only 2 tools, the set is minimal but still appropriate for the server's focused purpose of web search and content fetching. It is not excessive or overly thin.

    Completeness4/5

    The tool set covers the core operations for web interaction: searching and fetching. While there could be additional tools like raw HTML fetching or link checking, the current scope is reasonably complete.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • 1 of 1 community issues answered or closed 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 MIT 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?

    With no annotations provided, the description carries the burden of behavioral disclosure. It mentions using Mozilla Readability, but does not disclose whether JavaScript executes, rate limits, authentication needs, error handling, or what happens with non-HTML content.

    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?

    Single sentence that is front-loaded with the core action. No redundant words. Every part contributes meaning.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool is simple but lacks an output schema or description of the return format (e.g., plain text, markdown). Given no output schema, the description should mention the output structure. It is adequate but incomplete for an agent to fully understand the behavior.

    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 coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema: the URL parameter's description is already present, and max_chars has default/range in the schema. No additional context on why to adjust max_chars or what the extraction output looks like.

    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 verb 'Fetch and extract', the resource 'readable content from a web page URL', and the method 'using Mozilla Readability'. It effectively distinguishes from the sibling 'web_search' which is for searching, not fetching specific pages.

    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 the tool is for extracting content from a single URL, but lacks explicit guidance on when to use it versus alternatives. No mention of when not to use, prerequisites, or comparisons to 'web_search'.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    The description mentions the tool uses a 'local SearxNG instance', which hints at infrastructure but does not disclose rate limits, authentication, error handling, or other behavioral traits. There are no annotations to supplement, so the description carries the burden but only partially meets it.

    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 that front-load the core purpose and output. No unnecessary words. Efficient communication.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With 8 parameters and no output schema, the description provides basic purpose but lacks context on parameter interactions, result structure (beyond mentioned fields), or differentiation from sibling. Adequate but not thorough.

    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 coverage is 100% with clear parameter descriptions. The description adds no new parameter details beyond summarizing the return format. Baseline 3 applies given full schema coverage.

    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's action ('Search the web'), specifies the resource ('local SearxNG instance'), and describes the output ('titles, URLs, and snippets'). It effectively distinguishes from the sibling tool 'web_fetch' (page fetching vs. search).

    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 general web searching but does not provide explicit guidance on when to use this tool over siblings (e.g., for multi-result search vs. single-page fetch). No when-not-to-use or alternatives 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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  • Evaluate tool definition quality.

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