matlab-mcp-server-python

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 effectively describes key behaviors: it writes code to a temporary file (implying file system interaction), runs mcp_checkcode() (specifying the underlying function), and returns a structured list of issues. However, it doesn't cover aspects like error handling, performance implications, or side effects (e.g., whether temporary files persist).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is highly concise and well-structured in two sentences: the first states the purpose and method, the second details the process and output. Every sentence adds critical information without redundancy, making it easy to parse and front-loaded with key details.

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

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

Given the tool's moderate complexity (code analysis), no annotations, and an output schema (which covers return values), the description is largely complete. It explains the linting process, temporary file handling, and output structure. However, it lacks details on error cases or limitations (e.g., MATLAB version requirements), which would enhance completeness.

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

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

The input schema has 0% description coverage, so the description must compensate. It implicitly defines the 'code' parameter as MATLAB code to be linted, which adds essential semantic context beyond the schema's type information. However, it doesn't specify constraints (e.g., code length, syntax requirements) or examples, leaving some gaps.

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

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 ('lint MATLAB code') and resources ('checkcode/mlint'), distinguishing it from siblings like execute_code (which runs code) or read_script (which reads code). It specifies the exact analysis method and output format, making the purpose unambiguous.

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

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 prerequisites (e.g., needing MATLAB code), exclusions (e.g., not for other languages), or comparisons to siblings like execute_code (for running code) or read_script (for viewing code). Usage is implied but not explicitly stated.

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