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nanoparse-dev

nanoparse-mcp

Official
by nanoparse-dev
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TypeScript
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Server Quality Checklist

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

  • Latest release: v1.0.6

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or overlap. The single tool has a clearly defined purpose of fetching and converting web pages to markdown.

    Naming Consistency5/5

    The tool name follows a consistent verb_noun pattern: 'nanoparse_fetch'. This is clear and predictable, though with only one tool, consistency is trivial.

    Tool Count3/5

    Having only one tool feels thin for a server, even for a focused purpose. While the tool covers a specific function, agents might expect additional related operations or options.

    Completeness5/5

    For the stated purpose of fetching a web page and returning structured markdown with metadata, the single tool provides complete coverage. No other operations are necessary to fulfill the server's promise.

  • Average 4.3/5 across 1 of 1 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
    • 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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  ]
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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

  • Behavior4/5

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

    With no annotations provided, the description discloses important behavioral traits: first 50 parses free per IP, then payment via USDC on Base through x402, and no account/API key required. This goes beyond the schema and provides useful operational context, though it omits details like error handling or rate limits.

    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 two sentences, front-loaded with the main purpose, and includes necessary pricing/authentication details without unnecessary elaboration. Every sentence contributes value.

    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 tool is simple with only 2 parameters and no output schema. The description covers the return format (Markdown with metadata), the free tier, payment cost, and lack of API key requirement, which is complete for the tool's complexity. The debug parameter is documented in the schema, so no gap.

    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?

    Both parameters (url, debug) have descriptions in the schema, covering 100% of the parameters. The description itself does not add specific parameter semantics but complements with info about the output format. 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 fetches a web page and returns clean, structured Markdown with metadata. It provides a specific verb ('Fetch'), resource ('web page'), and output ('Markdown with metadata'), which is sufficient even without sibling tools.

    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 implies usage for converting web pages to Markdown, and includes practical context about free tier and payment. However, it does not explicitly describe when to use it over alternatives, though no siblings exist. Clear context without exclusions.

    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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Glama performs regular codebase and documentation scans to:

  • 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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