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KylinMountain

web-fetch-mcp

by KylinMountain
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: summarize gives an overview, compare contrasts multiple URLs, and extract pulls specific details. No ambiguity between them.

    Naming Consistency5/5

    All tool names follow the same verb_web pattern (summarize_web, compare_web, extract_web), making the set predictable and easy to navigate.

    Tool Count5/5

    Three tools is appropriately scoped for a web-content analysis server. Each tool addresses a distinct need without unnecessary bloat.

    Completeness4/5

    The toolset covers the core operations of summarization, comparison, and extraction. A minor gap is the absence of a raw fetch tool, but the server's purpose is focused on processed output, so this is acceptable.

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

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates the tool uses natural language prompts, but does not mention any limitations, side effects, or operational constraints. The description is merely a functional statement with no transparency beyond that.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, front-loaded sentence with no redundant information. It is efficient and appropriately sized for a tool with one parameter, though it could have been slightly more informative without losing conciseness.

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

    Completeness2/5

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

    There is no output schema, so the description should clarify what the tool returns, but it does not. No sibling tool context is provided, and no usage examples or limitations are mentioned. For a tool with minimal annotations and no output schema, the description is incomplete.

    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 fully documents the single 'prompt' parameter with a clear description (including URLs and extraction instructions), so schema coverage is 100%. The tool description itself adds no additional meaning beyond what the schema already provides, making the baseline of 3 appropriate.

    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 clearly states the verb 'Extract' and resource 'web content', indicating a specific extraction action. It distinguishes from sibling tools by the nature of the operation (extract vs summarize/compare), though it does not explicitly name alternatives.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus alternatives like summarize_web or compare_web. The description only states what it does, without any contextual cues for usage decisions.

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

  • 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 of behavioral disclosure. It only restates the action without explaining how URLs are processed, what the summary output looks like, or any side effects or limitations beyond the schema's mention of up to 20 URLs.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, front-loaded sentence with no wasted words: 'Summarize content from one or more URLs.' It is appropriately brief, though the brevity does limit the contextual value it provides.

    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?

    Given one parameter and no output schema, the description is minimally adequate but leaves gaps: it does not describe the output format, usage distinctions from extract_web/compare_web, or behavioral caveats. The schema compensates for URL limits and prompt structure.

    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 has 100% coverage for the single prompt parameter, describing it as a natural language prompt containing URLs and summarization instructions. The tool description adds no parameter-level meaning beyond this, so baseline 3 applies.

    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 clearly states the core action—summarize—and the resource—content from URLs. It is specific enough to distinguish from sibling tools like extract_web and compare_web, though it does not explicitly name those alternatives.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    There is no guidance on when to use this tool vs. alternatives, no prerequisites, and no exclusions. The description simply says what it does, leaving the agent to infer usage context from the tool name and schema.

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

  • 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 only states the high-level action with no details on input limits, output format, error behavior, or whether multiple URLs are required as opposed to optional.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, front-loaded sentence with no wasted words. It is appropriately concise for a simple tool, though it could be seen as under-specified in terms of what 'compare' entails.

    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?

    For a tool with one parameter and no output schema, the description gives the essential purpose and the schema provides invocation details. However, it lacks usage context (e.g., what output to expect) and does not mention any constraints beyond what is in the schema.

    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 description covers the single parameter well: 'Natural language prompt containing URLs (up to 20) and comparison instructions'. The tool description adds no additional parameter context, but since schema coverage is 100%, 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's purpose with a specific verb ('Compare') and resource ('content from multiple URLs'). It distinguishes itself from sibling tools (summarize_web, extract_web) by indicating a distinct action.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. There is no mention of when comparison would be preferred over summarization or extraction, and no exclusion criteria are given.

    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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