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redup-ai

redup.mcp-web-parser

Official
by redup-ai
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
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  • Latest release: v0.3.1

  • Disambiguation5/5

    The two tools are cleanly separated: parse_page handles HTML pages and returns markdown, while fetch_binary handles file downloads and returns metadata plus bytes. They also include when-to-use and when-not-to-use guidance, plus a clear fallback path when parse_page detects binary content.

    Naming Consistency5/5

    Both tools use the verb_noun pattern: parse_page and fetch_binary. Naming is regular, consistent, and immediately communicates what each tool does.

    Tool Count4/5

    Two tools is lower than the typical 3-15 range, but it maps exactly to the server's intended scope: HTML parsing and binary fetching. The set is slightly thin but not excessive or insufficient for such a narrow purpose.

    Completeness5/5

    The tool set covers the main web resource categories: HTML pages are parsed to markdown, and binaries are downloaded with metadata and bytes. The is_binary fallback closes the biggest edge case, so there are no obvious dead ends.

  • Average 4.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 12 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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

  • Behavior5/5

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

    The description adds behavioral context beyond annotations. It discloses the is_binary detection mechanism and the instruction to switch tools without retry loops. It also mentions the timeout clamping behavior. These are beyond what readOnlyHint and openWorldHint provide, and nothing contradicts the annotations.

    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?

    The description is compact and well-organized with clear sections and a bullet-like list of return fields. The main purpose is front-loaded, and every sentence adds value—usage instructions, output fields, and binary handling—without redundancy.

    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?

    The description lists all return JSON fields, covers edge cases (binary detection and switching), mentions the timeout behavior, and provides clear usage guidance. With the output schema present, the description still adds valuable operational context, making it complete for an agent to use effectively.

    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 covers 100% of the parameter descriptions (url and timeout). The tool description does not add extra parameter semantics beyond the schema, but it reinforces the URL type by mentioning 'HTML articles' and excluding binary files. Since schema coverage is high, the baseline score of 3 is appropriate.

    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 reads an HTML web page and returns cleaned markdown as JSON. It distinguishes itself from the sibling tool fetch_binary by specifying when to use it (HTML pages) and when not (binary files). The verb 'read' plus the resource 'HTML web page' makes the purpose very specific.

    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?

    Explicit WHEN TO USE and WHEN NOT TO USE sections are provided, naming the alternative tool fetch_binary. It also gives a specific fallback instruction: if is_binary=true, switch to fetch_binary and do not retry parse_page in a loop. This completely covers when and when not to use the 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?

    The description discloses key behavioral aspects: it performs a download only (no text extraction, OCR, or unzip), places bytes in JSON 'content_base64' rather than writing to a filesystem, and notes potential large size. It complements the annotations (readOnly, idempotent, non-destructive) with concrete details, exceeding the minimum required transparency.

    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?

    The description is well-organized with distinct sections (description, when to use, when not to use, download only, returns) and every sentence adds value. It avoids redundancy while covering necessary details, maintaining a concise yet complete structure.

    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?

    Beyond the annotations and schema, the description includes the list of return fields, explains the tool's role relative to parse_page, and specifies that it does not interact with a filesystem. This fully contextualizes the tool within its environment, making it self-sufficient for correct usage.

    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?

    The schema provides clear descriptions for both parameters: 'url' is specified as an http(s) URL for a binary file (non-HTML), and 'timeout' is described with units and the clamping behavior. The tool description reinforces the URL semantics, ensuring full coverage of parameter meaning.

    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: downloading a binary file and returning metadata and base64 bytes as JSON. It uses specific verbs ('Download', 'return') and specifies the resource type and output format, effectively distinguishing it from the sibling tool parse_page.

    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?

    The 'WHEN TO USE' and 'WHEN NOT TO USE' sections provide explicit conditions, including types of content (pdfs, zips, images) and when to prefer parse_page. It also advises against using this tool as a fallback for parse_page failures, covering both use and non-use 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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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