repair

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

With no annotations, the description carries the full burden of behavioral disclosure. It openly states that the LLM can delete fabricated segments, replace them, or decline to touch them, and that it returns structured repair actions with confidence and reasoning. This gives the agent a solid picture of the tool's autonomy and output without relying on annotations.

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 three tight sentences that front-load the primary action, then add behavioral detail and output shape. Every sentence earns its place with no redundancy or filler.

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 tool with no output schema, the description adequately explains the input context (flagged segments), the behavior (delete/replace/decline), and the output shape (structured actions with confidence and reasoning). Slightly more detail about whether actions are applied directly or returned as suggestions would be helpful, but it is not essential for correct invocation.

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 100%, and the schema already documents 'transcript_json' as a JSON string of a TranscriptResult with flags. The description adds workflow context but does not elaborate on the parameter itself, so it neither harms nor significantly improves parameter understanding.

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 ('Run the LLM repair loop') and a clear resource ('flagged segments'), while also enumerating the possible actions the LLM can take (delete, replace, or decline). This clearly differentiates 'repair' from siblings like 'detect_hallucinations' or 'score', which operate at earlier or different stages.

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 phrase 'on flagged segments' provides clear context that this tool is intended for transcripts that have already been flagged, presumably by 'detect_hallucinations'. It does not explicitly name alternatives or state when not to use it, but the context is clear enough for an agent to route correctly.

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