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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct output format (image, PDF, markdown) with no overlap in functionality, making selection unambiguous.

    Naming Consistency5/5

    All tools follow the consistent prefix 'snapforge_' followed by a clear noun describing the output, forming a predictable pattern.

    Tool Count5/5

    Three tools is an appropriate scope for a server focused on converting web content to common formats—neither too few nor too many.

    Completeness4/5

    Covers the three most common output conversions (image, PDF, markdown), though additional options like plain text or specific rendering parameters are absent.

  • Average 3.7/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 17 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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      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

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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

  • Behavior1/5

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

    Annotations declare readOnlyHint=true, but the description explicitly says 'PDF is written to a temp file and its path is returned', which indicates a write operation. This is a direct contradiction. The description fails to align with annotations, providing misleading behavioral cues.

    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 primary action, and contains no fluff. Every word contributes to understanding the core functionality.

    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 complexity (9 parameters, output schema), the description covers the main action but omits important context like parameter relationships (e.g., url vs html exclusivity) and lifecycle of the temp file. It is adequate but not rich.

    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 baseline is 3. The description adds no supplemental information about parameters (e.g., mutual exclusivity of url and html, default values). It does not exceed the value already provided by the schema.

    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 ('Render') and resource ('public URL or raw HTML') and output ('PDF, path returned'). It inherently distinguishes from sibling tools which render to screenshot or markdown, making the purpose unambiguous.

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

    Usage Guidelines3/5

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

    The description does not explicitly state when to use this tool over siblings (snapforge_screenshot, snapforge_markdown) or any prerequisites. Usage context is only implied by the tool name and output format; no guidance on exclusions or alternatives is provided.

    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?

    Annotations already declare readOnlyHint=true and destructiveHint=false, making the read-only nature clear. The description adds only that full-page is the default, which is a minor behavioral detail. There is no contradiction, but the description does not significantly enhance transparency beyond 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 extremely concise, consisting of two short sentences that front-load the core purpose. Every word serves a purpose, with no redundancy or filler. It is well-structured for quick parsing by an AI agent.

    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 the tool has 10 parameters and a complex input schema, the description is too sparse. It only highlights the full-page default, ignoring other important configurable options like delay, scale, width, height, and waitUntil. While the output schema may document return values, the description fails to guide the agent on how to effectively use the tool's capabilities.

    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 every parameter already has a description in the input schema. The tool description does not add any additional meaning or usage context for parameters like delay, scale, or waitUntil. Thus, it meets the baseline but adds no extra semantic value.

    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 ('Capture a screenshot'), the resource ('public URL or raw HTML'), and the output format ('image (PNG/JPEG)'). It also mentions 'Full-page by default,' which adds specificity. This distinguishes it from sibling tools snapforge_pdf and snapforge_markdown, which handle different output types.

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

    Usage Guidelines3/5

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

    The description implies usage for capturing visual representations of web content, but it does not explicitly state when to use this tool versus alternatives like snapforge_pdf or snapforge_markdown. No 'when-not' or exclusion criteria are provided, leaving the agent to infer context from the tool name and siblings.

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

  • Behavior4/5

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

    Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral context by mentioning 'article extraction + HTML→Markdown', which implies content processing beyond simple conversion. No contradictions. No mention of rate limits or auth, but acceptable given annotation coverage.

    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 core action, and contains no unnecessary words. Every sentence adds value.

    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 simplicity of the tool (4 parameters, output schema exists), the description is fairly complete. It explains the core transformation and a primary use case. Lacks mention of error conditions or output details, but the output schema covers return format.

    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 parameters are already well-documented. The description adds little beyond reiterating that 'url' or 'html' are alternatives, which is already in the schema. Baseline 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 extracts a clean Markdown version of a public URL or raw HTML, and distinguishes itself from sibling tools (screenshot, PDF) by focusing on text extraction. The verb 'Extract' and resource 'Markdown version' are specific.

    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 a clear use case ('great for feeding live web content to an LLM') and implicitly distinguishes from siblings by format (Markdown vs screenshot/PDF). However, it does not explicitly state when not to use or name alternatives, limiting full guidance.

    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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