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Server Quality Checklist

75%
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  • Latest release: v1.0.1

  • Disambiguation2/5

    The two tools are nearly identical; capture_full_page is explicitly a wrapper for capture_screenshot with full-page enabled. An agent would likely misuse them, as the difference is only a parameter.

    Naming Consistency3/5

    Both use verb_noun pattern ('capture_screenshot', 'capture_full_page'), but 'full_page' is a qualifier while 'screenshot' is the resource; inconsistent because one tool name specifies a parameter in the name itself.

    Tool Count3/5

    Two tools is minimal but arguably sufficient for a simple screenshot service. However, the duplication suggests one tool could have been omitted, making the surface slightly too heavy for the scope.

    Completeness3/5

    The set covers basic screenshot needs with features like viewport sizing, proxies, and ad removal. However, it lacks tools for specific device emulation or batch processing, which are common in screenshot services.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 11 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the burden of behavioral disclosure. It states the tool renders in a real Chromium browser and automatically removes ads and cookie banners, which is helpful. However, it does not mention potential side effects, rate limits, execution time, or authentication requirements. It also does not clarify whether the screenshot is destructive or what happens to the browser instance after capture.

    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 two sentences long, making it relatively concise. The first sentence states the core action, and the second lists major features. It avoids extraneous details but could be slightly more compact by combining the two sentences or trimming the feature list slightly.

    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 complexity of the tool (13 parameters, no output schema), the description provides a high-level overview of capabilities but lacks detail on return format (e.g., image type, resolution), error handling, and how features like 'full_page' work in practice. It is adequate for an experienced user but incomplete for a novice.

    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 all 13 parameters with descriptions, achieving 100% coverage. The tool description reiterates some schema concepts (viewport sizing, full-page capture, country proxies) but does not add significant new meaning beyond what the schema already provides. For example, 'country' parameter is explained in the schema; the description only mentions 'country proxies' generically. Baseline 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states it takes a screenshot of a web page using Site-Shot and returns an image. It mentions features like viewport sizing, full-page capture, and ad removal. However, it does not explicitly distinguish itself from the sibling tool 'capture_full_page', which may cause confusion.

    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 lists features but provides no guidance on when to use this tool versus alternatives like 'capture_full_page'. It does not mention any prerequisites or conditions for use, nor does it explain when to use the 'full_page' parameter or when to prefer a different tool.

    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?

    No annotations are provided, so the description carries the full burden. It describes the action as a wrapper but does not disclose side effects, output format details, or limitations beyond what the schema parameters cover.

    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?

    Two sentences with no wasted words; the core purpose is front-loaded.

    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 has 12 parameters (all well-described in schema) and no output schema, the description is adequate as a summary but lacks details on return format and additional behavioral context.

    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 coverage is 100% with detailed parameter descriptions, so the description adds little extra meaning. 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 takes a full-page screenshot and mentions it's a convenience wrapper around capture_screenshot with full-page capture enabled, distinguishing it from the sibling tool.

    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 full-page screenshots and references the sibling tool, but does not explicitly state when not to use it or provide alternative 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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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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