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niradler

diagram-mcp

by niradler
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

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

  • Latest release: v0.0.3

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: one renders Mermaid diagrams, the other renders Plotly charts. There is no overlap in functionality, so an agent can easily select the correct tool based on the desired output type.

    Naming Consistency5/5

    Both tools follow a consistent 'render_' prefix naming pattern, clearly indicating their action and distinguishing them by the library they support (mermaid vs plotly). This makes the naming predictable and easy to understand.

    Tool Count4/5

    With only two tools, the count is on the lower side but appropriate for a server focused on rendering two distinct visualization libraries. Each tool is justified by the different input formats and rendering engines, so the count feels reasonable for the scope.

    Completeness4/5

    The server covers the core functionality for rendering diagrams from two popular libraries. While it could include additional libraries or a generic render tool, the current set is sufficient for its stated purpose and has no obvious gaps in the rendering workflows.

  • Average 4/5 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 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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    {
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  ]
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    Then . Browse examples.

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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?

    Annotations are minimal (only title) with no destructive or readOnly hints. The description adds value by explaining output behaviors (localhost URL, file saving, raw data) and hints at non-destructive rendering. However, it does not disclose potential side effects, authorization needs, or whether the tool modifies existing resources. With no annotations, the description carries the burden but is not fully transparent.

    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 first sentence, followed by a bullet-like list of output options and examples. It front-loads the core purpose. The examples section is slightly lengthy but informative. Overall efficient with minimal redundancy.

    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 high complexity (16 parameters, nested objects, no output schema), the description is incomplete. It fails to explain most parameters, does not describe return value structure for each output type, and provides no guidance on sequencing with sibling tools. The examples help but do not compensate for the missing detail on half of the parameters.

    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 0%, so the description must compensate. It mentions a few parameters (theme, backgroundColor, format, output) in examples but does not explain over 10 parameters (e.g., quality, darkMode, filePath, sequence, flowchart options). The description says 'comprehensive styling and output options' but lacks detail, leaving many parameters ambiguous.

    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 'Render a Mermaid diagram' with specific output formats (SVG, PNG, JPG, PDF) and mentions styling options. The verb 'render' and resource 'Mermaid diagram' are specific and distinct from sibling tool 'render_plotly' which suggests Plotly charts.

    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 provides clear context on output options (link, filepath, raw) and includes examples for common use cases. However, it does not explicitly state when to use this tool versus the sibling 'render_plotly', though the diagram type implies it. No explicit exclusions or alternatives are given.

    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?

    With no annotations beyond title, the description effectively discloses key behaviors: output types (link returns localhost URL, filepath saves to disk, raw returns base64), custom styling options, and examples. It does not mention error cases or side effects, but the rendering behavior is well explained.

    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 well-structured with bullet points and examples. It is relatively long but each section adds value: purpose, output options, multiple examples. Front-loaded with the main purpose.

    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 has 12 parameters and no output schema, the description covers the required plotlyCode, output formats, styling, and return behavior for each output type. It could mention that the localhost URL is temporary or that file path requires write permissions, but overall is complete enough.

    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 adds significant meaning by explaining output options, format, and styling parameters (backgroundColor, width, height). The example code illustrates the required plotlyCode structure, compensating for the lack of per-parameter 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 it renders Plotly charts to multiple formats (SVG, PNG, JPG, PDF). It distinguishes from sibling tool render_mermaid by focusing on Plotly, a different charting library.

    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 provides explicit usage guidance through examples for different output options (link, filepath, raw) and mentions the required plotlyCode. It implicitly differentiates from render_mermaid but does not explicitly state when not to use this tool.

    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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