Plan or apply LLM fixes

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

Goes well beyond the annotations by disclosing that dry_run=False sends affected code to a third-party provider (reinforcing openWorldHint), edits files in place (reinforcing destructiveHint), requires the skylos[llm] extra and credentials, does not run tests, and demands review. This is exactly the behavioral context annotations alone cannot convey.

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?

Front-loaded with the core purpose and mode split, and most sentences carry real information. It is somewhat long, and the trailing local-CLI/`analyze` note, while useful, is tangential to invoking this tool.

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 high-risk mutation tool with 6 parameters and an output schema, the description covers the necessary prerequisites, data-flow implications, and post-run caution. Return values are handled by the output schema, so nothing essential is missing.

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%, so all six parameters are already documented in the schema, establishing a baseline of 3. The description reinforces the dry_run and model semantics and adds the credential/environment angle, but adds little syntax or format detail beyond what the schema already provides.

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?

States a specific verb (plan or apply LLM-generated fixes) and resource (Skylos findings in a project), and clearly splits the two operating modes. It does not, however, distinguish itself from the nearby sibling `generate_fix`, which sounds like it may overlap, leaving the agent to infer the boundary.

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?

Gives explicit conditional context: dry_run=True plans only, dry_run=False edits in place, and it names prerequisites (skylos[llm], provider config/credentials, SKYLOS_API_KEY) plus what happens without a key (only `analyze` works, 5 calls/day). It lacks an explicit statement of when to prefer this over `generate_fix` or `validate_code_change`, so it falls short of a full 5.

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