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FraanW

MATLAB MCP Server

by FraanW
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: one generates MATLAB code and the other executes it. There is no ambiguity or overlap in their purposes.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern: generate_matlab_code and execute_matlab_code. The naming is perfectly consistent and predictable.

    Tool Count3/5

    Two tools is on the low end and feels thin for a server, but each tool has a meaningful role. The set is coherent, yet minimal.

    Completeness4/5

    The core workflow of generating and executing MATLAB code is covered without dead ends. Some auxiliary capabilities like persistent workspace management or file I/O are missing, but agents can likely work around these with generated code.

  • Average 2.9/5 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 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, the description carries the full behavioral disclosure burden. It states the basic action and result but does not disclose execution side effects, persistence between calls, result format, or how saveScript and scriptPath affect behavior. This is thin for an arbitrary code-execution tool.

    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 one compact sentence with no filler and the primary action is front-loaded. It is concise, though arguably too terse given the missing behavioral context.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with one required parameter and fully documented optional parameters, the schema plus description are mostly usable. However, with no output schema and no annotations, the vague phrase 'return the results' leaves the return format undefined, and no execution-environment context is given.

    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 each parameter is already documented in the schema. The description adds no additional meaning for 'code', 'saveScript', or 'scriptPath', so the baseline 3 applies.

    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 uses a specific verb ('Execute'), identifies the target ('MATLAB code'), and notes the outcome ('return the results'). It is clear on its face but does not explicitly distinguish this tool from its sibling generate_matlab_code, so it stops short of a 5.

    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?

    No guidance is given about when to choose execute_matlab_code over generate_matlab_code, and there are no exclusions or prerequisites. The intended use must be inferred from the tool name and basic semantics rather than from explicit direction.

    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 only says code will be generated; it does not explain whether code is returned, saved, or how the optional saveScript/scriptPath parameters affect behavior. It also does not state that this tool does not execute the generated MATLAB code.

    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, front-loaded sentence with no filler. It communicates the primary function efficiently, and every word adds value.

    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?

    For a tool with no annotations and no output schema, the description leaves important operational details unstated: how the generated code is returned to the agent, what the default saving behavior is, and how it relates to execute_matlab_code. These gaps could cause incorrect invocation or misunderstanding of the tool's response.

    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 already covers all three parameters with descriptions, so the baseline is 3. The description's 'from a natural language description' slightly reinforces the central 'description' parameter, but it adds no meaning beyond what the schema already provides.

    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 uses a specific verb ('Generate') and resource ('MATLAB code'), making the core purpose clear. It does not explicitly contrast itself against the sibling execute_matlab_code, so the differentiation is only implicit.

    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?

    There is no guidance about when to choose this tool over execute_matlab_code, and no mention of prerequisites or intended workflow. The agent must infer that 'generate' means code creation rather than execution, but the description offers no explicit selection criteria.

    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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  • Evaluate tool definition quality.

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