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Dutta-SD

web-fetch-mcp

by Dutta-SD
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools, fetch and screenshot, have clearly distinct purposes: one retrieves content (markdown, text, HTML) and the other captures a visual rendering. There is no overlap in functionality or ambiguity.

    Naming Consistency5/5

    Both tool names are single, lowercase verbs describing the action performed. This is a simple and consistent naming pattern that is easy to understand and predict.

    Tool Count4/5

    With only two tools, the server is minimal but focused. The fetch tool is highly parameterized and covers many use cases, while screenshot adds visual capture. The count is appropriate for a specialized tool set, though slightly thin.

    Completeness3/5

    The server covers the core operations of fetching web content and capturing screenshots. However, it lacks utilities for tasks like URL validation, link extraction, or metadata retrieval, which would round out a typical web fetching toolset.

  • Average 5/5 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
    • 51 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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    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

  • Behavior5/5

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

    Discloses key behaviors: renders JS, escalates strategies, follows redirects, converts to markdown, and raises FetchBlocked instead of silently failing. Adds context beyond readOnlyHint annotation.

    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?

    Well-structured with sections, bullet points, and examples. Slightly verbose but every sentence adds value. Front-loaded with core purpose.

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

    Completeness5/5

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

    Covers all aspects: when to use, how it works (tiers), parameters, return format, error handling, and examples. Complete despite no schema descriptions and presence of output schema.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema coverage, description fully explains all 7 parameters, including defaults, examples, and behavior for each. Exactly compensates for lack of schema descriptions.

    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?

    Clearly states 'Fetch the contents of a web page' and emphasizes it's the primary web-fetch tool, distinguishing from generic alternatives. Specifies verb+resource+scope.

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

    Usage Guidelines5/5

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

    Provides explicit WHEN TO USE and WHEN NOT TO USE sections with concrete examples, including a direct alternative (web search tool).

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

  • Behavior5/5

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

    Describes real Chrome engine, optional proxy, full page vs viewport, wait time, dismiss selector. No contradiction with readOnlyHint annotation.

    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?

    Well-structured with one sentence purpose, usage guidance, then arg list with clear descriptions. No fluff.

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

    Completeness5/5

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

    Covers all parameters, return type, and usage context. No output schema but explains return as MCP Image inline. Adequate for 7-param tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Despite 0% schema description coverage, the description explains all 7 parameters in detail, including defaults, behavior, and constraints (e.g., proxy format).

    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?

    Clear verb 'Render' + resource 'web page' + output 'PNG screenshot'. Distinguishes from sibling 'fetch' by being visual counterpart. Explicit usage cues.

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

    Usage Guidelines5/5

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

    States when to use: user asks to show/screenshot, visual state matters. Explicitly compares to fetch and mentions proxy support for anti-bot scenarios.

    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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