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ryanmazzolini

Minimal Godot MCP

by ryanmazzolini
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Server Quality Checklist

67%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_diagnostics targets individual GDScript files, while scan_workspace_diagnostics covers the entire workspace. Their descriptions explicitly differentiate them and advise on when to use each, eliminating any potential for confusion.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_diagnostics and scan_workspace_diagnostics) with clear, descriptive names that reflect their scope and function. There are no deviations in naming style.

    Tool Count3/5

    With only 2 tools, the server feels thin for a Godot MCP, as it covers only diagnostics retrieval without broader operations like code execution, project management, or asset handling. However, the tools are well-defined for their specific diagnostic purpose.

    Completeness2/5

    The server is severely incomplete for a Godot development environment, lacking tools for core tasks such as running projects, editing scenes, managing nodes, or handling assets. It only provides diagnostics, which is a narrow slice of the domain, leaving significant gaps for agent workflows.

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

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

    • 0 of 5 community issues answered or closed in the last 6 months
    • 3 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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?

    Describes it as a read operation returning specific data types, and mentions a prerequisite. No annotations are present, so the description carries the burden and does so adequately without contradictions.

    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 concise sentences that front-load the main action and provide necessary context 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?

    For a tool with 3 optional parameters and no output schema, the description explains the return value and a prerequisite, covering most gaps. Could mention limits or default behavior but is sufficient.

    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 100%, so the baseline is 3. The description adds no additional meaning beyond the schema, but the schema itself is clear.

    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 verb 'Get' and the resource 'console output from Godot debug session', and distinguishes from sibling tools like clear_console_output and get_diagnostics.

    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?

    Provides clear use cases (debug runtime behavior, check print output, monitor warnings) and a prerequisite (active debug session), but does not explicitly mention when not to use or alternatives.

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

  • Behavior3/5

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

    The description accurately describes the destructive action but provides no additional behavioral context beyond the basic operation, such as irreversibility or 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two concise sentences that front-load the core action and follow with usage guidance, with no wasted words.

    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?

    Given the tool's simplicity (no parameters, no output schema), the description fully covers its purpose and when to use it, leaving no gaps.

    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?

    With zero parameters, the description has no parameter information to add, but schema coverage is 100%, so baseline 4 is appropriate.

    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 action ('Clear the console output buffer') and the resource, distinguishing it from sibling tools like get_console_output.

    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 advises to use before running a specific test or action, providing clear context for when to invoke this tool over alternatives.

    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, the description carries full burden. It lists the types of issues returned and mentions execution time (<1s) and the use of Godot LSP. It does not explicitly state side effects (likely none), but it is clear that the operation is a diagnostic check.

    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 three sentences with no wasted words. It front-loads the purpose and output, then provides usage context and alternatives. Every sentence adds value.

    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 simple one-parameter diagnostic tool without an output schema, the description covers the purpose, return types (listing specific errors), usage conditions, and alternatives. No gaps are apparent.

    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 100%, so the schema already describes the parameter. The description does not add any additional semantics about the file_path parameter beyond what the schema provides, meeting the baseline of 3.

    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 verb 'Check', the resource 'GDScript files', and the specific outputs (syntax errors, type errors, etc.). It also distinguishes from the sibling tool 'scan_workspace_diagnostics' by directing workspace-wide validation to that tool.

    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 this tool: when user references .gd files, requests modifications, or reports errors. Also explicitly states when not to use it and directs to the sibling tool for workspace-wide validation.

    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 burden. It discloses scope, exclusions, and performance (~1-2s). While it implies a read operation, it does not explicitly state non-destructiveness, but context suggests it.

    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, front-loaded with key information, no wasted words. Efficiently conveys purpose, scope, performance, and alternative.

    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?

    Given no parameters, no output schema, and simple operation, the description covers all needed context: what, where, when, performance, and alternative.

    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?

    No parameters exist, so schema coverage is 100%. The description adds context about what is scanned and excluded, which is sufficient.

    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 scans the entire workspace for GDScript errors across all .gd files, excluding addons/ and .godot/. It distinguishes from the sibling tool get_diagnostics for single-file checks.

    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?

    It provides explicit guidance: use for finding all errors/warnings in the project, and for single-file checks, use get_diagnostics instead. Also includes time estimates.

    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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