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hwang-yh-cto

space-ocr-mcp

by hwang-yh-cto
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.3

  • Disambiguation5/5

    The two tools have completely distinct purposes: one extracts data from images, the other lists available templates. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tools use snake_case and follow a verb_noun pattern (ocr_extract, list_templates). However, 'ocr_extract' is slightly redundant since the server name already includes OCR, but it's still clear.

    Tool Count3/5

    Only 2 tools for an OCR server is minimal but acceptable given the focused scope on extracting structured data using built-in templates. Could benefit from additional management tools.

    Completeness4/5

    The set covers listing templates and extracting data, which are the core operations. Missing functionality like template management or field validation is minor given the server's narrow purpose.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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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

  • Behavior4/5

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

    No annotations, so description carries burden. Clearly indicates read-only list operation. No contradictory or missing behavioral info for such a simple tool.

    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, front-loaded purpose, no waste. Efficient and clear.

    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?

    Completeness is high: no parameters, simple output, sibling tool context given. Covers everything needed for selection and basic usage.

    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?

    No parameters; schema coverage 100%. Description adds value by explaining purpose and linking to sibling, meeting the baseline for zero-parameter tools.

    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?

    Describes listing all built-in document templates, a specific verb+resource. Distinguishes from sibling ocr_extract by mentioning the returned id is used as template_id.

    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?

    States to use returned id as template_id in ocr_extract, providing clear context. No explicit when-not-to-use, but sufficient given sibling context.

    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?

    No annotations provided, so description discloses internal method (space-ocr), output structure (JSON with bounding boxes), and ensures non-hallucinated data. Does not discuss auth or rate limits, but sufficient for a read tool.

    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 concise sentences front-load the purpose, then detail two usage paths. Every sentence adds value with no redundancy.

    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?

    Despite no output schema, description explains return format (JSON with extracted fields and bounding boxes) and quality guarantee. Covers all key behavioral aspects for a complex extraction tool.

    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?

    With 100% schema description coverage, baseline is 3. Description adds value by clarifying that template_id auto-resolves fields/prompt, fields are for custom schemas, and prompt overrides. Adds context beyond 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 data from document images, listing example types (receipt, invoice, ID) and distinguishes from sibling list_templates by specifying extraction behavior.

    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?

    Provides guidance on using template_id for built-in types and fields for custom schemas, plus a hint to call list_templates(). Lacks explicit when-not-to-use, but context is clear.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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