get_complexity_metrics

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

With no annotations provided, the description carries full burden. It clearly explains the metrics, risk levels, and the prerequisite of load_project. It implies a read-only behavior by simply getting metrics, and the output is described, providing adequate transparency beyond the schema.

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 well-structured with sections for purpose, usage, output, metrics, risk levels, and prerequisite. It is concise, front-loaded, and every sentence adds value without redundancy.

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 no output schema and 3 parameters, the description covers the core functionality, input example, metric details, and a prerequisite. It could be improved by explicitly stating the output format (e.g., JSON structure), but the provided details are sufficient for basic usage.

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?

Schema coverage is 100%, so the baseline is 3. The description does not add new meaning to parameters beyond the schema; it only shows an example usage with filePath. The metrics explanation is helpful but does not clarify granularity or includeDetails.

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 identifies the tool as retrieving cyclomatic complexity, cognitive complexity, and LOC, with a risk assessment. This distinguishes it from sibling analysis tools like analyze_control_flow or analyze_file, which focus on broader or different metrics.

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 includes a usage example and importantly notes that load_project must be called first, which is a key prerequisite. However, it does not explicitly guide when to use this tool over alternatives or when not to use it, leaving some ambiguity.

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