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avatrix1

StructureAI MCP Server

by avatrix1
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JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools. The single tool has a clear, distinct purpose.

    Naming Consistency5/5

    The tool name 'extract_structured_data' follows a clear verb_noun pattern (snake_case) and is descriptive. With a single tool, consistency is trivially perfect.

    Tool Count4/5

    One tool is minimal but appropriate for a focused server that handles multiple extraction schemas through a single interface. It is slightly under the typical well-scoped range but still reasonable.

    Completeness4/5

    The tool covers the core functionality of extracting structured data from text for several common schemas. Minor gaps like a status or usage check are not critical, and the tool is complete for its stated purpose.

  • Average 4.1/5 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

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  • This repository includes a README.md file.

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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

  • 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 indicates extraction is safe (read-only implied) and mentions free tier limitations. However, it lacks details on error handling, rate limits beyond the free tier, and behavior for unsupported or malformed input.

    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: two sentences front-loading the core purpose and then providing essential supplementary info. Every sentence is useful with no filler.

    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?

    The tool has no output schema, so the description should ideally describe the returned JSON structure. It only states 'structured JSON' vaguely. The description is adequate for basic use but leaves out output format details and error scenarios.

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

    Parameters4/5

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

    Schema coverage is 100%, so parameters are already documented. The description adds value by explaining the api_key parameter's purpose and how to obtain one, and it clarifies the supported schemas beyond the enum values.

    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 structured JSON from unstructured text and lists supported schemas (receipt, invoice, email, resume, contact, custom). It directly defines the verb, resource, and scope without ambiguity.

    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 context on limitations (free tier of 10 requests) and how to obtain an API key for more requests. While there are no sibling tools to distinguish, this guidance helps the agent understand usage constraints.

    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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  • Evaluate tool definition quality.

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