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Server Quality Checklist

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  • Latest release: v0.1.3

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or ambiguity among tools.

    Naming Consistency5/5

    With a single tool, naming consistency is not an issue; the name 'extract_structured_data' is clear and descriptive.

    Tool Count3/5

    The server has only one tool, which is thin for most domains. However, as a specialized extraction service, it is borderline acceptable.

    Completeness4/5

    The single tool covers the core extraction use case well, but lacks supporting features like schema management or pagination support, leaving minor gaps.

  • Average 5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior5/5

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

    With no annotations provided, the description fully covers behavioral traits. It explains the extraction process (schema-guided, handles JS with Browserless), return format (validated typed JSON), and what is returned ('extracted fields, confidence score, item count, and tokens used'). It also discloses that it only works on public pages, setting clear expectations.

    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 efficiently structured: it starts with the primary purpose, then elaborates on features and ideal use cases, and ends with return values. Every sentence adds unique information without redundancy. It is front-loaded with the most important detail (what it extracts) and fits within a few sentences.

    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?

    Given the tool's complexity (3 parameters, one being a complex object schema) and no output schema, the description is remarkably complete. It covers all input parameters with usage guidance, explains the extraction engine's capabilities, and lists the output fields. No critical information is missing for an agent to decide and invoke the tool correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description adds significant value beyond the schema's property descriptions. For 'url', it specifies must be public and no login-protected. For 'schema', it provides supported types, examples, and notes nested object support. For 'mode', it explains each enum value with concrete use cases. This extra context is critical for correct usage.

    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's action: 'Extract structured JSON from any public webpage' using a schema-guided engine. It specifies the verb (extract), resource (structured JSON), and context (public webpages, JavaScript-heavy handling). Since there are no sibling tools, it effectively distinguishes its purpose.

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

    Usage Guidelines5/5

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

    The description explicitly states when to use the tool: 'Ideal for scraping product pages, articles, job listings, company data, search results, and any other structured web content.' It also clarifies limitations: 'Does not support login-protected or paywalled pages.' The mode parameter description provides additional guidance on when to use 'single' vs 'list' modes.

    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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