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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavioral context: it is static analysis, does not run a real build, and a clean result is not a deploy guarantee. This is valuable beyond the structured annotations, though rate limits or auth behavior are not mentioned.

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 dense but every clause earns its place: purpose, footgun list, static-analysis caveat, and the GitHub alternative are all stated without redundancy. It is front-loaded with the primary action and stays appropriately sized for a tool with this much behavioral nuance.

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 single-parameter static-analysis tool, the description covers input format, scan scope, and result semantics, including the 'not a deploy guarantee' caveat. With no output schema, the exact shape of findings is left unspecified, but the core information needed to invoke the tool correctly is present.

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 schema covers the single files parameter 100%, so the baseline is 3. The description adds extra value by providing a concrete marker format example and telling the user which files to include (api/*.py plus root manifests), going beyond the schema's generic description.

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 starts with a specific verb and resource ('Scan pasted project files') and enumerates concrete footgun categories, making the tool's scope unmistakable. It also distinguishes itself from sibling check tools by focusing on Vercel Python-runtime deploy issues. The 'static analysis' clarification further prevents confusion with build/deploy tools.

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 clearly states the intended input modality ('pasted project files') and explicitly routes public GitHub repo scans to an external paid checkout. It does not explicitly say when not to use the tool (e.g., for non-Python projects or when a real build is needed), but the limitation is disclosed.

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