delete_ticket_logs

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

No annotations are provided, so the description carries the full burden. It discloses exactly what gets deleted: ticket vectors from the store and, by default, locally downloaded raw log files. It lacks explicit irreversibility or permission notes but is not misleading.

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 two sentences, front-loaded with a clear summary and followed by concise elaboration. Every sentence adds value and there is no 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 destructive tool with no annotations and no output schema, the description covers the key facts: what is deleted, when to run it, and the default raw-file behavior. It could mention irreversibility or error cases, but it is largely complete for its simplicity.

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 connects delete_raw to 'by default the raw log files' and connects ticket_id to 'the ticket's vectors'. This adds meaningful semantics beyond the bare schema.

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 opens with a specific action ('Clean up a ticket') and clearly identifies the resources being affected: embeddings and raw log files. This distinguishes it from sibling tools like ingest_ticket_logs, query_logs, and get_log_stats.

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?

It gives an explicit temporal context: 'after the pipeline completes', and explains the intended benefit ('freeing disk and clearing stale data'). It does not name explicit alternatives or when-not-to-use cases, but the context is clear enough for an agent to decide.

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