check_const_correctness

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 the tool checks const correctness and returns a report with suggestions, but lacks details on execution behavior, such as whether it performs static analysis, runtime checks, error handling, or performance implications. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded, starting with the main purpose followed by parameter and return details in a structured format. It avoids unnecessary words, but could be slightly more concise by integrating the parameter and return sections more seamlessly. Every sentence adds value, making it efficient.

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 complexity (analyzing C++ code for const correctness), the description is minimally adequate. It covers the purpose, parameter, and return value, and an output schema exists, so it doesn't need to explain return values in detail. However, with no annotations and incomplete behavioral details, it leaves gaps in understanding how the tool operates or interacts with other tools.

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 description adds meaningful context beyond the input schema. The schema only defines 'code' as a string with 0% description coverage, but the description specifies it as '要检查的 C++ 代码' (C++ code to check), clarifying the parameter's purpose and expected content. This compensates well for the low schema coverage, though it doesn't detail format or constraints like code length or language version.

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: '检查 C++ 代码中的 const 正确性' (Check const correctness in C++ code). It specifies the verb ('检查' - check) and resource ('C++ 代码中的 const 正确性' - const correctness in C++ code), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'analyze_memory_safety' or 'suggest_modern_cpp', which might also involve C++ code analysis, so it doesn't reach the highest score.

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 any prerequisites, context for usage, or comparisons to sibling tools such as 'check_naming' or 'suggest_modern_cpp'. Without this information, users might struggle to select the appropriate tool for their needs.

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