Skip to main content
Glama
coffe-d

MCP Vision Server

by coffe-d
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    Both tools describe images, but one outputs to terminal and the other saves to file. Their purposes are clearly distinguished, though the core functionality overlaps.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'describe_image' and 'describe_image_to_file'. The naming is predictable and clear.

    Tool Count3/5

    With only two tools, the server feels thin but covers the basic need of describing images and optionally saving results. It is borderline for the typical 3-15 tool range.

    Completeness2/5

    The tool surface is minimal and lacks features like batch processing, model selection, or other vision tasks. Agents have no way to adjust output formats beyond file writing, leaving notable gaps.

  • Average 3.8/5 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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?

    With no annotations, the description is the sole source of behavioral info. It explains the tool returns AI description but does not disclose potential issues like file access errors, rate limits, or whether the operation is read-only (though reasonable to infer). Basic but adequate.

    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?

    Very concise: one sentence for purpose followed by a clear parameter list. No redundant information, and every sentence is necessary.

    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?

    For a simple image description tool with an output schema (not shown), the description covers the main behavior and parameters. It lacks error handling info but is reasonably complete for 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?

    Despite 0% schema coverage, the description adds value by explaining each parameter: image_path requires absolute path and supported formats, prompt allows custom instruction, max_tokens sets output length with default. This compensates for lack of schema descriptions.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    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 detailed description. It specifies input as local absolute path. However, it does not explicitly differentiate from sibling tool 'describe_image_to_file', though the return mechanism (direct vs file) is implied.

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

    Usage Guidelines2/5

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

    No guidance on when to use this tool vs alternatives like 'describe_image_to_file', nor any prerequisites or constraints such as file accessibility or supported formats beyond the path.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden. It discloses writing UTF-8 files, default output path, and parameters. However, it does not mention overwrite behavior, error handling, or permission requirements, which are relevant for a file-writing tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise, with a clear opening statement and a bullet list for parameters. It fronts the main purpose and encoding benefit. Minor improvement could be more structured, but it is efficient.

    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?

    The description covers the tool's core function, default behavior, and parameter details. Given an output schema exists (not shown), it does not need to explain return values. It is complete for a file-writing tool with a sibling, though could add error conditions.

    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 description coverage is 0%, so the description must compensate. It explains each parameter's purpose (image_path absolute, output_path optional default, prompt custom, max_tokens limit). While not exhaustive (e.g., missing image format constraints), it adds meaning beyond the schema titles.

    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 saves to a file, solving Windows terminal encoding issues. It explicitly distinguishes itself from the sibling tool 'describe_image' by focusing on file output.

    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 explains when to use this tool (to avoid encoding issues, for file output) and implies the alternative (describe_image) for terminal display. However, it does not explicitly state when not to use or provide exclusion criteria.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

MCP-Vision-Server MCP server

Copy to your README.md:

Score Badge

MCP-Vision-Server MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/coffe-d/MCP-Vision-Server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server