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chupre

yandex-vision-ocr-mcp

by chupre
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.3.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for images and single-page PDFs (synchronous), the other for multi-page/large PDFs (asynchronous). No overlap in functionality.

    Naming Consistency4/5

    Both tools follow the 'recognize_' prefix pattern with clear suffixes ('pdf' and 'text'), making them predictable and self-explanatory.

    Tool Count4/5

    Two tools is minimal but appropriate for a focused OCR server. It covers the core distinction between image/single-page and multi-page PDF processing without unnecessary complexity.

    Completeness4/5

    The tool surface covers the essential OCR use cases: image and PDF text recognition. The model parameter handles handwritten, table, and markdown, so no major gaps.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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.

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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?

    With no annotations, the description transparently discloses the asynchronous recognition process (recognizeTextAsync + getRecognition polling) and language limitations (no auto-detect). This is adequate disclosure for a non-destructive read operation, though rate limits or size constraints are not mentioned.

    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 two sentences, front-loading the core purpose and immediately following with usage guidance. Every sentence contributes essential information without redundancy.

    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 (6 parameters, async polling, no output schema), the description covers the key behavioral aspects and parameter choices. It explains the async workflow and language limitations, but could have elaborated on the polling mechanism or return value format. Still, it is largely complete for an AI agent.

    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 description coverage is 100%, so the baseline is 3. The description adds minor value by summarizing model purposes (e.g., 'page-column-sort' for multi-column text) and the choice between path and base64, but these details are already present in the schema. No significant new information is provided.

    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 explicitly states it recognizes text in a PDF document using Yandex Vision OCR, with details on async processing. This clearly distinguishes it from the sibling 'recognize_text' tool, which likely handles non-PDF formats.

    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 indicates use for PDFs and large files, providing context on when to apply it. While it doesn't explicitly list when not to use it or contrast with alternatives, the guidance is clear and sufficient for the intended use case.

    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?

    With no annotations, the description carries full burden. It discloses synchronous operation, supported MIME types, model behaviors, and language limitations (no auto-detect, only ru/en). It could mention error handling or response size, but overall transparency is good.

    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 a single efficient paragraph of 3-4 sentences, front-loaded with core purpose. Every sentence adds value: format support, model options, input alternatives, and sibling tool reference. 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 6 parameters, 100% schema coverage, and no output schema, the description covers input sources, model options, language limits, and sibling tool. It omits error handling and output format details (but format parameter covers that). Adequate for the tool's simplicity.

    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%, so baseline is 3. The description adds context about model use cases and mutual exclusivity of path/base64, but does not significantly deepen understanding beyond schema.

    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 purpose: recognize text in images or single-page PDFs using Yandex Vision OCR (synchronous). It specifies supported formats and distinguishes from the sibling tool 'recognize_pdf' by noting multi-page PDFs should use that tool.

    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 provides explicit guidance on when to use this tool vs 'recognize_pdf' for multi-page/large PDFs. It also explains input options (path vs base64), model selection, and output formats, giving clear usage context.

    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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