analyze_python_smells

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

With no annotations, the description must carry the burden of disclosing behavior. It notes the cli mode requires local Python and advanced_pyexamine installation, but does not state whether the analysis is read-only, may modify files, or has other side effects. This is insufficient for an analysis tool.

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?

Two sentences, no fluff. The main function is front-loaded, and the cli prerequisite is an important behavioral note. It's appropriately compact.

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?

For a tool with 4 parameters and no output schema, the description gives a high-level overview and a prerequisite, but lacks details about result structure or workflow integration. It's adequate for basic use but not fully complete given the sibling complexity.

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 covers 100% of parameters with descriptions, and the description adds no parameter-specific semantics beyond the schema. Baseline of 3 applies.

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 it analyzes a Python project using advanced_pyexamine and returns smell results as JSON. The verb 'analyze' distinguishes it from sibling 'get' tools that retrieve existing results, though it doesn't explicitly name alternatives.

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?

No explicit guidance on when to use this tool versus siblings like get_latest_pyexamine_result or get_smells_by_file. The description implies it's for running a new analysis but lacks clear context and exclusions.

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