freshness_audit

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

With no annotations provided, the description carries the full burden. It discloses the analysis method (static AST, deterministic), implies read-only behavior, and describes the return format (JSON string with violations, severity, replacements). This is solid transparency for an analysis tool, though it could mention limitations or prerequisites.

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 compact and well-structured: a front-loaded purpose sentence, a methodological note, then Args and Returns sections. Every sentence earns its place with no redundant text.

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 two parameters and an output schema, the description covers purpose, method, parameter semantics, and return value. It is nearly complete, but could add a note about what makes documentation 'authoritative' or any system prerequisites. Overall, an agent has enough to call it correctly.

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 description coverage is 0%, so the description must compensate. It fully explains both parameters: 'code' is the Python source snippet or module to audit, and 'project_dir' is the root directory for grounding version context. This adds meaning beyond the schema's bare property titles.

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 states a specific verb and resource: 'Analyze Python code to detect deprecated APIs, version mismatches, and outdated patterns.' This clearly distinguishes it from siblings like inspect_stack and resolve_constraints, which address different concerns.

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 clear context on what the tool does and its approach ('deterministic static AST analysis grounded in authoritative documentation'), implying when it should be used for freshness auditing. However, it does not explicitly name alternatives or state when not to use it, so it falls short of the highest bar.

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