python_check_types

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

With no annotations, the description should reveal behavioral traits. It only mentions support for watch and verbose modes but doesn't explain their effects (e.g., watch mode continuously runs). It doesn't indicate whether the tool is destructive or requires certain dependencies.

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 extremely concise and front-loaded with the key action. However, it may be too brief to provide sufficient context for an AI agent, but within the scoring of conciseness, it is 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?

The description is minimal and does not provide contextual completeness for a tool with 5 parameters and no annotations. It lacks information about prerequisites (pyright installation), default behavior, or how it fits into a workflow with sibling tools like python_lint.

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 already describes all parameters (100% coverage). The description only reiterates the existence of watch and verbose modes but adds no additional semantic detail beyond the schema, thus meeting the baseline.

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 function: checking Python types with pyright, and mentions specific modes (watch, verbose). It differentiates from sibling tools like python_lint which focuses on linting.

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 sibling tools such as python_lint or python_test. It does not mention prerequisites, limitations, or appropriate contexts.

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