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dh1011

Auto Favicon MCP Server

by dh1011
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one processes a local PNG file, while the other downloads an image from a URL. There is no ambiguity or overlap in their functionality, making it easy for an agent to choose the correct tool based on the input source.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'generate_favicon_from_' prefix, using snake_case throughout. The naming is predictable and clearly indicates the action (generate) and the source (png or url), ensuring readability and coherence.

    Tool Count3/5

    With only 2 tools, the server feels thin for a favicon generation domain, as it might lack operations like validation, customization (e.g., size adjustments), or cleanup. However, it covers the core functionality of generating favicons from different sources, which is reasonable but minimal.

    Completeness3/5

    The server covers the basic generation from PNG and URL sources, but there are notable gaps such as support for other image formats (e.g., JPEG, SVG), customization options (e.g., specifying sizes), or error handling tools. This limits flexibility but allows for core workflows.

  • Average 3.5/5 across 2 of 2 tools scored. Lowest: 2.9/5.

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

    • 0 of 1 community issues answered or closed 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 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"
  ]
}

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. While it states what the tool does (generates favicon files), it doesn't describe important behavioral aspects like what happens if the PNG is invalid, whether the output directory must exist, if existing files are overwritten, performance characteristics, or error handling. The return message description is minimal.

    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 appropriately sized with three clear sections: purpose statement, parameters, and return value. Each sentence serves a purpose, though the parameter descriptions could be more informative. The structure is logical and front-loaded with the main purpose.

    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?

    Given the tool has 2 parameters with 0% schema coverage and an output schema exists, the description provides basic parameter semantics and return information. However, for a file generation tool with no annotations, it lacks details about file formats generated, quality considerations, error conditions, and sibling tool differentiation. The output schema likely covers return structure, but behavioral context is incomplete.

    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 description includes an 'Args' section that lists and briefly describes both parameters, adding meaning beyond the schema which has 0% description coverage. However, the parameter descriptions are minimal ('Path to the PNG image file' and 'Directory where favicon files will be generated') and don't provide format details, constraints, or examples. This partially compensates for the schema gap but not fully.

    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's purpose: 'Generate a complete favicon set from a PNG image file.' This specifies the verb (generate), resource (favicon set), and source format (PNG image). However, it doesn't explicitly differentiate from its sibling 'generate_favicon_from_url' beyond the source format difference, which is implied but not stated.

    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?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'generate_favicon_from_url' or explain scenarios where one would prefer PNG file input over URL input. There's no context about prerequisites, limitations, or typical use cases.

    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 the full burden. It mentions downloading an image and generating files, but lacks details on permissions, rate limits, error handling, or what happens if the URL is invalid. This is a significant gap for a tool that performs network operations and file generation.

    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 appropriately sized and front-loaded, with a clear purpose statement followed by concise parameter and return value explanations. Every sentence adds value without waste.

    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 (network download and file generation) and no annotations, the description covers the basic operation and parameters. With an output schema present, it need not explain return values in detail, but it could benefit from more behavioral context like error cases or dependencies.

    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?

    The description adds meaning beyond the input schema by explaining that 'image_url' is for downloading an image and 'output_path' is where favicon files will be generated. With 0% schema description coverage, this compensates well, though it could include format details like supported image types or path requirements.

    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 with a specific verb ('generate') and resource ('complete favicon set'), and distinguishes it from its sibling tool 'generate_favicon_from_png' by specifying it downloads from a URL rather than using a PNG file.

    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 implies usage context by specifying 'from a URL,' which differentiates it from the sibling tool that likely uses a PNG file. However, it does not explicitly state when to use this tool versus alternatives or any exclusions.

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

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