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SapienEx-AI

urltomarkdown-mcp

by SapienEx-AI
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation5/5

    Both tools convert content to Markdown but accept fundamentally different inputs: raw HTML vs. a URL. Their purposes are clearly distinct, with no overlap.

    Naming Consistency5/5

    Both tool names follow the consistent 'convert_<source>_to_markdown' pattern, using snake_case and clear verb-noun structure.

    Tool Count4/5

    With only two tools, the server is minimal but well-scoped for its domain of converting to Markdown. The count matches the simple purpose.

    Completeness4/5

    The server covers the two primary input types: raw HTML and URL. It is lacking file-based input, but for a specialized MCP server, this coverage is reasonable.

  • Average 3.4/5 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 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 to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    No annotations are provided, so the description must disclose behavioral traits. It only says 'clean Markdown' without details on safety, idempotency, errors, or side effects. The conversion process, potential data loss, or formatting changes are not mentioned.

    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 extremely concise at one sentence, front-loading the purpose. Every word earns its place, though it omits beneficial context that could be added without significant bloat.

    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?

    Given no output schema and no annotations, the description should explain what 'clean Markdown' entails (e.g., handling of styles, scripts, tables). It lacks details on return format, error behavior, or parameter interactions, making it incomplete for an agent to fully anticipate 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 description coverage is 100%, so the schema already documents all five parameters. The description adds no extra meaning or context beyond what the schema provides, earning the baseline score.

    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 (convert) and the resource (HTML to Markdown). It distinguishes the tool from the sibling 'convert_url_to_markdown' by specifying 'raw HTML' input, implying the sibling handles URLs.

    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 explicit guidance on when to use this tool versus the sibling 'convert_url_to_markdown'. The description does not mention prerequisites, limitations, or when not to use it.

    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, the description must fully disclose behavior. It mentions 'fetch' implying network access, but omits details like rate limits, timeouts, caching, or error handling. The parameter 'readability' is not explained in context, though documented in the schema. The description is minimal but not misleading.

    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 a single, well-formed sentence that communicates the core functionality without superfluous words. It is front-loaded with the action and resource, making it easy to parse quickly.

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

    Completeness4/5

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

    Given the absence of an output schema, the description should clarify the return format. 'Clean Markdown' implies a string, but it's ambiguous. The tool is simple with 4 parameters and no nested objects, so the description covers the main action. Lacking error or edge case info prevents a perfect score.

    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% description coverage, so the schema already provides parameter meanings. The tool description adds no additional detail beyond the schema, resulting in a baseline score of 3. No extra semantics are offered.

    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 action ('fetch and convert') and the resource ('URL web page content') and result ('clean Markdown'). It effectively distinguishes from the sibling 'convert_html_to_markdown' by specifying URL fetching as the input method.

    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 lacks explicit guidance on when to use this tool versus the sibling 'convert_html_to_markdown'. No context is provided on prerequisites, preferred scenarios, or exclusion criteria, leaving the agent to infer usage from the tool name alone.

    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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