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mambalabsdev

mcp-page-finder-extractor

by mambalabsdev

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity or risk of selecting between overlapping functions. The tool's purpose is clearly defined and distinct.

    Naming Consistency4/5

    The tool name 'find_company_page' follows a clear verb_noun pattern, but with only one tool, consistency across a set cannot be evaluated. The name is descriptive and appropriate.

    Tool Count3/5

    The server has exactly one tool, which is minimal and feels thin. While the tool is comprehensive, the server could benefit from additional tools (e.g., listing all available page types or managing extraction settings) to feel more complete.

    Completeness4/5

    The tool covers page discovery and extraction for 46 page types, which is thorough for its domain. However, it lacks a way to programmatically enumerate the full list of supported page types, and no other operations are exposed, leaving a minor gap.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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.

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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

  • Behavior5/5

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

    Annotations already declare readOnly/openWorld/idempotent, and the description builds substantially on them: discovery method order, 'guessing a URL path is the LAST method tried and is scored 0.6 or below,' found=false vs null semantics, browser never used against blocks/CAPTCHAs, one row per input, APIFY_TOKEN and credit consumption. This is exemplary behavioral disclosure.

    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 long but information-dense and front-loaded. The opening sentence states core behavior, and every subsequent sentence adds load-bearing operational detail: available page types, discovery heuristics, confidence thresholds, output semantics, costs, and auth. No wasted words.

    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 tool's complexity (14 params, no output schema), the description covers the key behavioral context: return values (URL, method, confidence), interpretations of found=false/null, coverage/fetch_status, and what locate_and_extract adds. It doesn't enumerate all response fields, but it describes the critical ones sufficiently.

    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 all 14 parameters documented, so baseline is 3. The description adds contextual meaning (mode costs extra events, companies path runs identity resolution, knownUrls skips discovery) but does not need to re-explain parameter syntax since the schema already does that thoroughly.

    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 first sentence is a specific verb+resource: 'Give it a company domain and name a page type. It finds that page on the company's own website and returns the URL, the method, and a confidence.' This unambiguously states what the tool does and distinguishes it from generic web search 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 provides clear context: when to use locate vs locate_and_extract, 'Use domains instead whenever you hold a domain,' 'ask for what you will use' for pageTypes, and knownUrls to skip discovery. No explicit exclusions or alternatives are needed since there are no sibling tools, but the guidance is strong.

    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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