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beznazwiska

@codetoimage/mcp-server

by beznazwiska
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one returns inline image data, the other a temporary URL. Their descriptions explicitly contrast when to use each, leaving no ambiguity.

    Naming Consistency5/5

    Both tool names follow the exact pattern 'render_html_to_X', where X is the output type (image vs url). The naming is perfectly consistent and descriptive.

    Tool Count5/5

    With only two tools, the server is tightly scoped to its single purpose of rendering HTML to images. Each tool covers a necessary output format, so the count is ideal.

    Completeness5/5

    For a server focused solely on rendering HTML to images, offering both inline and URL outputs covers all common use cases. There are no missing features or dead ends.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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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  • 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

  • Behavior4/5

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

    Discloses key behavioral aspects: returns a URL valid for 24 hours, supports template_id/variables/preset inputs. No annotations are present, so description carries full burden. Could mention idempotency or side effects, but generally transparent.

    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?

    Two sentences that are dense with information: first sentence defines purpose, second provides usage context and constraints (24-hour validity). No unnecessary words, front-loaded with key action.

    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 11 parameters (0 required), nested objects, and no output schema, the description covers core functionality and use cases. It explains URL lifecycle and sibling relationship. Could be improved by mentioning optional width/height override of preset and output format, but overall adequate.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is low (27%). Description only highlights template_id, variables, and preset, ignoring other parameters like css, html, width, format, transparent. The schema provides some descriptions (e.g., for preset), but the tool description adds minimal value beyond that.

    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 action: render HTML/CSS to an image and return a temporary hosted URL. It distinguishes from sibling by noting 'instead of inline bytes', providing a specific verb-resource pair.

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

    Usage Guidelines5/5

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

    Explicitly states when to use the tool: when the caller needs to reference the image elsewhere (e.g., Instagram Graph API, OpenGraph, Slack). Also mentions that it supports the same inputs as the sibling tool render_html_to_image, aiding decision.

    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?

    No annotations provided, so description carries full burden. It explains return format (inline image), mentions paid plans for transparency, and describes placeholder behavior. Lacks details on rate limits or performance, but generally transparent for a rendering 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?

    Single dense paragraph, front-loaded with main purpose. Every sentence adds new information without redundancy. Could be slightly more structured (e.g., bullet points), but highly efficient.

    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?

    Despite no output schema, the description explains what happens with the rendered image. Covers all major features: inline return, template usage, presets, variables, background, transparency (paid). Complete enough for correct tool invocation.

    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 coverage is 100%, baseline 3. Description adds value by explaining the two input modes (html vs template_id+variables), preset dimensions, and how variables fill placeholders. Provides more context than schema alone.

    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?

    Clearly states it renders HTML/CSS to image formats. Describes alternative input modes (template_id with variables) and preset sizes. Explicitly distinguishes from sibling tool render_html_to_url by specifying when to use each.

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

    Usage Guidelines5/5

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

    Provides explicit guidance: 'Use this when you need to see or hand back a finished image... If the caller needs a URL... use render_html_to_url instead.' Clearly defines context and alternative.

    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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