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zcag

readdown-mcp

by zcag
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: convert_html processes existing HTML strings, while fetch_markdown retrieves and processes web pages. There is no overlap in functionality, making tool selection straightforward for an agent.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (convert_html and fetch_markdown), using clear action verbs that describe their operations. The naming is predictable and aligned with their functions.

    Tool Count3/5

    With only two tools, the server feels minimal for a web content processing domain. While the tools cover core use cases, the scope is thin, potentially limiting agent workflows without additional operations like batch processing or format validation.

    Completeness4/5

    The tools provide essential coverage for converting HTML to Markdown, both from strings and web pages. However, there are minor gaps, such as no tool for reverse conversion (Markdown to HTML) or handling specific HTML elements, which agents might need to work around.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

  • 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

  • Behavior3/5

    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 tool's purpose and context but lacks details on behavioral traits like error handling, performance, or output characteristics (e.g., formatting specifics, limitations).

    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 front-loaded with the core purpose in the first sentence and adds a usage guideline in the second, with no wasted words. Every sentence contributes directly to understanding the tool.

    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 no annotations and no output schema, the description is adequate for basic understanding but lacks details on behavioral aspects and output format. It covers purpose and usage but does not fully compensate for the missing structured information.

    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 description coverage is 100%, so the schema fully documents all parameters. The description does not add any parameter-specific details beyond what the schema provides, meeting the baseline for high 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 specific action ('convert'), resource ('HTML string'), and outcome ('clean, LLM-optimized Markdown'), distinguishing it from the sibling 'fetch_markdown' which likely fetches content rather than converting existing HTML.

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

    Usage Guidelines4/5

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

    It provides explicit context for when to use ('when you already have HTML content and need it as Markdown'), which helps differentiate from 'fetch_markdown', but does not specify when not to use or mention alternative tools beyond the sibling.

    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?

    With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns article content with metadata and token count, and is 'much faster and lighter than browser-based solutions.' However, it lacks details on error handling, rate limits, authentication needs, or what 'clean, LLM-optimized' specifically entails.

    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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose, output, and key benefits. Every sentence adds value without redundancy, making it easy to scan and understand quickly.

    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 the tool's moderate complexity (3 parameters, no annotations, no output schema), the description is somewhat complete but has gaps. It explains the output includes metadata and token count, but without an output schema, it doesn't detail the return structure. For a tool performing web fetching and conversion, more behavioral context would be helpful.

    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 description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond what the schema provides, such as explaining the implications of 'raw' mode or 'include_header' in more detail. Baseline 3 is appropriate when schema does the heavy lifting.

    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 specific action ('fetch a web page and convert it to clean, LLM-optimized Markdown') and distinguishes it from the sibling tool 'convert_html' by emphasizing web fetching and LLM optimization. It explicitly mentions the resource (web page) and output format (Markdown).

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

    Usage Guidelines4/5

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

    The description provides clear context for when to use this tool ('fetch a web page... faster and lighter than browser-based solutions'), but does not explicitly state when not to use it or mention alternatives beyond the sibling tool. It implies usage for web content extraction with performance benefits.

    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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