Skip to main content
Glama

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.4.1

  • Disambiguation5/5

    With only a single tool, there is no possibility of confusing it with any other tool. The tool's purpose is clearly defined around image understanding.

    Naming Consistency5/5

    The tool follows a clear verb_noun pattern (see_image), and since it is the only tool, the naming is internally consistent.

    Tool Count3/5

    A single tool feels thin for a server, but the scope is narrow (image description). It is borderline but not an extreme mismatch.

    Completeness5/5

    The tool covers all input methods mentioned (local file, URL, clipboard, recent screenshot) and fulfills the stated purpose of describing images for models without multimodal input.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 24 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.

  • 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 provided, the description carries the full burden of transparency. It discloses that a vision model is used and that the output is a textual description, which covers the core behavior. However, it does not detail output format, error scenarios, or limitations such as clipboard availability beyond what the schema mentions. It also does not explicitly confirm the operation is read-only, though '读取' implies it.

    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, consisting of two short sentences that directly convey the function and usage context. It is front-loaded with the action and resource, and every word earns its place without any filler or repetition.

    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 tool with only two parameters and no output schema, the description, combined with the detailed schema, provides sufficient context: what it does, when to use it, and what it returns ('返回文字描述'). It lacks some optional behavioral details like error handling, but these are not critical for such a straightforward read operation.

    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?

    The input schema already provides comprehensive descriptions for both parameters, covering source types and the default prompt behavior, so the description adds little beyond what the schema states. The description's mention of source categories is redundant with the schema but consistent. Baseline of 3 is appropriate given the high schema coverage.

    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 function with a specific verb ('读取图片') and resource ('image' from local file, URL, clipboard, recent screenshot), and further clarifies its purpose ('用于主模型无多模态输入能力时的识图'). This distinguishes it from potential alternative approaches and leaves no ambiguity about what it does.

    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 explicitly states when to use the tool: when the main model lacks multimodal input capability. It also enumerates the types of image sources supported, providing practical context. However, it does not explicitly name alternative tools or exclusions, though the given context is sufficient for most use cases.

    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

j-can-see MCP server

Copy to your README.md:

Score Badge

j-can-see 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/PichurChill/j-can-see'

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