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WilliamCloudQi

MATLAB MCP Server

by WilliamCloudQi
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one executes existing MATLAB code, while the other generates new code from natural language. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (execute_matlab_code and generate_matlab_code), using the same verb style and snake_case formatting. This predictability aids in understanding and usage without any deviations.

    Tool Count2/5

    With only two tools, the server feels thin for a MATLAB domain, which typically involves more operations like plotting, data analysis, or file management. While the tools cover core execution and generation, the scope is limited and may not support complex agent workflows effectively.

    Completeness2/5

    The tool set is severely incomplete for a MATLAB server, lacking essential operations such as loading/saving data, creating plots, debugging code, or managing variables. Agents will face significant gaps when trying to perform common MATLAB tasks beyond basic code execution and generation.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed 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.

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool executes code and returns results, but lacks critical details such as execution environment (e.g., sandboxed, local MATLAB instance), safety considerations (e.g., code injection risks), performance traits (e.g., timeout limits), or error handling. This leaves significant gaps for an agent to understand how the tool behaves beyond its basic function.

    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 extremely concise and front-loaded, consisting of a single sentence that directly states the tool's core function. There is no wasted language or redundancy, making it efficient for an agent to parse. Every word earns its place by conveying essential information without unnecessary elaboration.

    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 the complexity of executing code (a potentially risky operation) and the lack of annotations and output schema, the description is insufficiently complete. It doesn't address critical context like execution safety, result format, error conditions, or dependencies. For a tool with no structured safety or output information, the description should provide more guidance to help an agent use it effectively and safely.

    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?

    The input schema has 100% description coverage, clearly documenting all three parameters. The description adds no additional parameter semantics beyond what the schema provides, such as code syntax requirements or path formatting. However, with high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter documentation.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    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 a specific verb ('Execute') and resource ('MATLAB code'), making it immediately understandable. It distinguishes from the sibling tool 'generate_matlab_code' by focusing on execution rather than generation. However, it doesn't specify what kind of results are returned or the execution environment, keeping it from a perfect score.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'generate_matlab_code' or any other potential tools, nor does it specify prerequisites, execution context, or limitations. The agent must infer usage based solely on the tool name and description.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical behavioral details: it doesn't mention whether the generated code is saved by default, what format or quality the output is in, any limitations (e.g., complexity, length), or error handling. For a code generation tool with zero annotation coverage, this is a significant gap.

    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 a single, clear sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a straightforward tool. Every word earns its place, making it highly efficient.

    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 the complexity of code generation (a non-trivial task), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., code string, file path, errors), any constraints on the input description, or how the saveScript and scriptPath parameters interact. The agent is left with significant gaps in understanding the tool's behavior and output.

    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 documents all three parameters (description, saveScript, scriptPath) with clear descriptions. The tool description adds no parameter-specific information beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Generate MATLAB code from a natural language description.' It specifies the verb ('generate'), resource ('MATLAB code'), and input source ('natural language description'). However, it doesn't explicitly differentiate from its sibling tool 'execute_matlab_code' (which runs code vs. generating it), so it falls short of a perfect score.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. There's no mention of the sibling tool 'execute_matlab_code' or any other context for choosing between generating code and executing it. The agent must infer usage from the tool name alone, which is insufficient.

    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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