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kobexi26

mcp-vision

by kobexi26
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: ocr_image extracts text only, while recognize_image describes image content. The descriptions explicitly contrast them, leaving no ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun snake_case pattern: 'ocr_image' and 'recognize_image'. This creates a predictable and clear naming convention.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a vision-focused service. While the tools cover basic OCR and general image recognition, the count is on the low end for the domain.

    Completeness3/5

    The server provides core text extraction and image description, but lacks other common vision operations like object detection, image generation, or format conversion. The surface is adequate for narrow use cases but not comprehensive.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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

  • Behavior3/5

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

    描述说明了行为（提取文字、保持排版），但未提及权限、副作用或结果格式等，在没有注解的情况下基本充分但不够详尽。

    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?

    描述简洁，先总述用途，再区分同族工具，最后说明参数和返回，无冗余内容。

    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?

    针对单一参数和简单返回值，描述充分解释了输入输出，无需额外补充。

    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?

    解释了image参数支持的类型（本地路径、URL、base64），超越了模式中仅字符串类型的定义，覆盖了参数。

    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?

    明确说明了OCR提取文字功能，并与recognize_image区分，使用了具体动词+资源（提取文字），场景清晰。

    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?

    通过对比recognize_image和列举适用场景（截图取字、票据录入等）提供了使用指引，但未明确说明何时不应使用。

    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 provided, the description carries the full burden. It discloses error conditions (FileNotFoundError, RuntimeError) and parameter behaviors (image source types, prompt default), adding useful context. It does not mention any destructive behavior, which is appropriate. Could further explain model dependencies or rate limits.

    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 well-structured with clear sections (Args, Returns, Raises) and bullet points. Every sentence adds value, and there is no redundancy. It is appropriately sized for the tool's complexity.

    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 existence of an output schema (mentioned), the description covers return values and error cases. It addresses parameter details and usage scenarios thoroughly. No critical information is missing for an agent to select 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 input schema has 0% description coverage, so the description fully compensates. It explains the 'image' parameter can be local path, URL, or base64, and describes 'prompt' as optional with a default. This adds significant meaning beyond the schema's bare definitions.

    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 recognizes image content and returns a textual description. It uses specific verbs and resources, and implicitly distinguishes from sibling 'ocr_image' by focusing on general image understanding rather than just OCR.

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

    Usage Guidelines3/5

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

    The description lists applicable scenarios (screenshot analysis, UI review, etc.), providing implicit usage guidance. However, it does not explicitly state when not to use the tool or contrast it with alternatives. The sibling tool 'ocr_image' is not mentioned, missing an opportunity for clear differentiation.

    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:

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

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