predict_impact

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

No annotations provided, so description carries full burden. It discloses the output format, algorithm basis (calls + imports + decision links), and lack of LLM call, but lacks details on prerequisites, edge cases, or performance implications.

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?

Single sentence is clear and front-loaded with key information. Could be slightly more concise but does not waste words.

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?

No output schema, but description explains output format and algorithm. Missing details on what 'modules' means and ranking criteria, but sufficient for a simple tool. Reasonably complete given context.

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 has 100% description coverage for both parameters. Description adds no extra meaning beyond reinforcing that 'file_path' is the file to analyze and 'repo' is optional. Baseline 3 is appropriate.

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 returns a ranked Markdown list of affected modules based on graph coupling. It specifies the verb 'returns', the resource 'modules', and the mechanism, distinguishing it from siblings like 'explain_change' or 'get_symbol_context'.

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 mentions 'No LLM call' implying fast deterministic output, but does not explicitly indicate when to use this tool over alternatives like 'explain_change' or 'get_symbol_context'. No usage scenarios or exclusions are given.

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