chatgpt_search_conversations

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

Annotations already declare readOnly, idempotent, non-destructive, open-world. The description adds genuine behavioral context beyond that: it scans titles only, matching is case-insensitive, and the complete=false flag signals an incomplete scan scope, which is important for correctly interpreting results.

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?

A single dense sentence, front-loaded with the core action, with two clarifying clauses (not body search, complete flag semantics). No waste.

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?

The search semantics and the complete flag are well covered, but with three parameters at 0% schema coverage and no output schema, the description leaves archived and max_scan behavior undocumented, so it is only adequate for the tool's complexity.

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% and the description explains none of the three parameters. It hints at pagination but never clarifies max_scan or the archived toggle, so an agent must infer these from the schema alone. This does not compensate for the missing schema documentation.

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 and resource: a paginated scan over conversation titles using case-insensitive substring matching. It explicitly distinguishes itself from body/content search, which differentiates it from get_conversation semantics, though it does not name list_conversations as the alternative.

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?

Usage is implied: use it for substring matching on titles, not full-text. The 'complete=false means incomplete scope' note guides interpretation of results, but there is no explicit when-to-use versus list_conversations or when-not guidance.

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