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lesonky

GCP Diagram MCP Server

by lesonky
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Python
Local

Server Quality Checklist

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

  • Latest release: v1.0.6

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: listing icons, providing examples, and generating diagrams. No overlap in functionality, making it easy for an agent to select the right tool.

    Naming Consistency5/5

    All tool names follow the verb_noun pattern in snake_case (generate_diagram, get_diagram_examples, list_icons), which is consistent and predictable.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its core mission: icon discovery, example learning, and diagram generation. This minimal set avoids unnecessary complexity.

    Completeness5/5

    The tool set covers the full workflow: discover icons, learn syntax via examples, and generate diagrams. There are no missing operations for the stated purpose of creating diagrams.

  • Average 4.7/5 across 3 of 3 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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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?

    With no annotations, the description fully explains execution details (show=False, runtime imports), return value (path to PNG), and code requirements. Minor gap: no mention of error handling or potential side effects.

    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?

    Well-structured with sections and bullet points; all content is relevant and front-loaded. Slightly long but justified by the complexity of the tool.

    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?

    Comprehensive coverage: explains prerequisites, code patterns, supported diagram types, return value, and all parameters. No output schema needed given the clear description of returned path and status.

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

    Parameters5/5

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

    Schema coverage is 100%, and the description adds significant context: code format, timeout default, filename generation, workspace_dir behavior (saves to 'generated-diagrams' subdirectory).

    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 generates a PNG diagram from Python code using the diagrams package. It distinguishes from sibling tools by referencing get_diagram_examples and list_icons as prerequisites.

    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 step-by-step usage instructions, including when to use this tool (after getting examples and listing icons), what not to do (never import), and important notes about the workspace_dir parameter.

    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?

    Without annotations, description bears full burden. It explains read-only nature by stating 'get example code' and details return type, though no explicit safety note.

    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?

    Structured with sections, front-loaded purpose, but slightly verbose with redundant explanatory sentences.

    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?

    For a single-parameter tool with no output schema, the description thoroughly covers purpose, categories, usage, and examples, leaving minimal ambiguity.

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

    Parameters5/5

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

    Schema coverage is 100% but description adds extensive context: lists categories with examples and explains what each demonstrates, far beyond the enum list.

    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 gets example code for diagram types, lists categories, and distinguishes from sibling tools like generate_diagram.

    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?

    Explicit step-by-step instructions are provided, along with when to use (learn syntax before creating) and an alternative tool link.

    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, but description explains it dynamically inspects the package and returns a hierarchical dictionary. Could mention performance considerations but otherwise transparent for a read-only inspection tool.

    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?

    Well-structured with clear sections (description, usage instructions, example workflow, returns). Every sentence is informative. No fluff.

    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?

    Even without an output schema, the description explains the return structure (dictionary with providers, services, icons hierarchically). Covers all essential information for correct use.

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

    Parameters5/5

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

    Schema coverage is 100% with concise descriptions. The description adds significant value by explaining the progressive filtering pattern and giving concrete examples of parameter values ("gcp", "compute").

    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 lists available icons from the diagrams package with optional filtering. It distinguishes from sibling tools (generate_diagram, get_diagram_examples) by focusing on icon discovery.

    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 detailed step-by-step usage instructions with examples, including the progressive filtering workflow. Tells the agent when to call with no filters, with provider_filter, and with both filters.

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

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