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basementstudio

MCP Three

by basementstudio
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

58%
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: get-model-structure is for debugging and analysis by returning parsed JSON structure, while gltfjsx is for code generation by converting models into React components. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.

    Naming Consistency4/5

    The tool names follow different conventions: get-model-structure uses kebab-case with a verb-noun pattern, while gltfjsx is a specific tool name without a clear verb. This minor deviation prevents a perfect score, but the names are still readable and descriptive enough for their functions.

    Tool Count3/5

    With only 2 tools, the server feels thin for handling GLTF/GLB models, as it might lack operations like model validation, editing, or export. However, the tools cover core tasks of analysis and code generation, making it borderline appropriate but potentially incomplete for broader workflows.

    Completeness3/5

    The server covers two key aspects: model structure analysis and React component generation. However, there are notable gaps, such as no tools for model creation, updating, or deletion, and no support for non-React frameworks or basic file operations, which could limit agent capabilities in full model lifecycle management.

  • Average 4.3/5 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 is failing

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  • 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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, non-mutating operation. The description adds valuable context beyond annotations by specifying the output format ('reusable, declarative React JSX component') and performance aspects ('optimal performance'), though it could mention more about error handling 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?

    The description is front-loaded with the core purpose, followed by supported features and use case, all in three concise sentences. Each sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

    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 (2 parameters with extensive nested options, no output schema), the description provides a solid overview of functionality and use case. However, it could be more complete by mentioning output details (e.g., file generation, error cases) or dependencies, though annotations cover safety aspects adequately.

    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 schema description coverage at 50%, the description compensates by listing key options ('TypeScript output, mesh/material instancing, pruning, compression, texture format, mesh simplification, and more') that align with the input schema properties. It provides a high-level overview of parameter purposes, though it doesn't detail all 16 nested options or their interactions.

    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 specific verbs ('Converts', 'Supports') and resources ('GLTF/GLB 3D model file', 'React (react-three-fiber) JSX component'). It distinguishes from the sibling tool 'get-model-structure' by focusing on conversion rather than analysis, and includes the benefits ('optimal performance and flexibility').

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

    Usage Guidelines3/5

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

    The description implies usage context ('Useful for integrating 3D assets into React apps') but does not explicitly state when to use this tool versus alternatives like the sibling 'get-model-structure' or other 3D processing tools. It mentions the target use case but lacks explicit comparisons or exclusions.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable context about what the tool does ('loads the file and returns the parsed scene structure as JSON, using GLTFStructureLoader from gltfjsx') and its intended use case ('complex model debugging'), which goes beyond the annotations. No contradiction with annotations.

    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 two sentences that are front-loaded with the core purpose, followed by usage guidelines. Each sentence adds clear value without redundancy, making it efficiently structured and appropriately sized for the tool's complexity.

    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 rich annotations (readOnly, idempotent, non-destructive) and 100% schema coverage, the description adds meaningful context about the tool's behavior and use case. However, there is no output schema, and the description does not detail the return format (e.g., JSON structure), which is a minor gap for a debugging tool.

    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%, with the parameter 'modelPath' fully documented in the schema. The description does not add any additional parameter details beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without extra value.

    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 resource ('structure of a GLTF/GLB model file'), and explicitly distinguishes from the sibling tool 'gltfjsx' by specifying 'For code generation use the gltfx tool.' This provides specific differentiation beyond just the tool name.

    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?

    The description provides explicit guidance on when to use this tool ('for complex model debugging and not implementation') and when to use an alternative ('For code generation use the gltfx tool'). This gives clear context for tool selection versus the sibling 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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