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LiuHao-1443

Stamp it

by LiuHao-1443
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.7

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one adds an image watermark, the other adds a text watermark. There is no ambiguity or overlap between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (add_image_watermark, add_text_watermark) with underscore separation, making naming predictable and clear.

    Tool Count4/5

    With only 2 tools, the server is very focused on watermarking. While minimal, it covers the two primary watermark types, so the count feels appropriate for a simple utility.

    Completeness4/5

    The server covers the core watermarking operations (image and text). Missing advanced options like opacity or position, but the essential functionality is complete for a basic stamping tool.

  • Average 3.5/5 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 Apache 2.0.

  • 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"
  ]
}

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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

  • Behavior2/5

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

    The description mentions it returns the processed image path but does not disclose behavioral traits such as whether the original image is modified, required image formats, or side effects. No annotations are provided to compensate, so the description carries the full burden and falls short.

    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 structure (Args, Returns). It uses no unnecessary words and fits the tool's simplicity. However, it could be more structured for multilingual clarity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple 3-parameter tool, the description covers the purpose and basic parameters. The return value is mentioned. Without the output schema, the description is adequate but does not elaborate on possible errors, output format, or other complexities. It meets the minimum viable standard.

    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 0%, but the description provides explanations for all three parameters: image_path (local file path), watermark_text (text content), and angle (tilt angle, default 30). This adds meaning beyond the schema's type/default definitions, partially compensating for the lack of schema descriptions.

    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 '为图片添加文字水印' (add text watermark to image), which is a specific verb+resource combination. It also distinguishes from the sibling tool 'add_image_watermark' by specifying 'text watermark' versus an image watermark.

    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?

    No explicit guidance is provided on when to use this tool versus alternatives (e.g., add_image_watermark) or any exclusions. The usage is implied by the tool name and description, but the agent receives no context about preferences or constraints.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It only mentions adding a watermark and saving the result, but does not disclose whether the original file is modified, what the output format is, or any permissions needed.

    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 very concise, with a one-line purpose statement followed by a clear Args section. No redundant information, and the structure is easy to parse.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    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, return values need not be detailed, but the description lacks details on watermark positioning, image format compatibility, or error handling. The tool's behavior is not fully specified.

    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 description coverage, the description includes an Args section that explains each parameter: image_path is a local file path, watermark_image_path is the watermark image path, and angle has a default of 30 degrees. This adds significant meaning beyond the schema property names.

    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 it adds an image watermark to a picture, distinguishing it from the sibling tool 'add_text_watermark' which adds text watermarks. The verb 'add' and resource 'image watermark' are specific.

    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 implies usage for image watermarking but does not explicitly state when to use this tool over the sibling 'add_text_watermark'. No when-not-to-use or alternative guidance is provided.

    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:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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