audit_test_assertions

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

No annotations are supplied, so the description carries the full burden of behavioral disclosure, and it reveals almost nothing: it does not say whether the analysis is read-only, what the output looks like (a list of findings? severities? line numbers?), or how it handles non-Python or assertion-free input. For an analysis tool with zero annotation coverage and no output schema, this is a substantial gap.

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?

A single front-loaded sentence with no filler; every word (including the enumeration of defect classes) contributes to selection. Nothing is padded or repeated.

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?

With no annotations, no output schema, and no explanation of return values or limitations, the definition is under-specified for an auditing tool whose whole value lies in its findings report. An agent cannot predict the response shape or judge whether the tool fits a given test file.

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% and there is only one parameter, so the schema already documents test_code fully. The description's phrase 'unit test assertions' implies the input should be test code containing assertions, adding marginal meaning, but it gives no hints about scope, size limits, or required framing.

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 names a specific verb ('Audits') and a specific resource ('unit test assertions') and adds the criteria being checked ('weakness, tautologies, or bad patterns'), so an agent knows exactly what the tool does. It stops short of distinguishing itself from the sibling generate_edge_case_tests, but the audit-vs-generate distinction is clear enough from the wording.

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?

There is no when-to-use guidance, no stated prerequisites (e.g., that the input must be a complete pytest/unittest file), and no mention of the sibling tools or when they would be preferred. The agent must infer all routing decisions from the one-line purpose.

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