list_queries

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

With no annotations, the description carries the burden. It discloses that results are recent and scoped to user prompts, but omits ordering, pagination, default limits, and whether it is read-only. This is adequate for a simple list tool but leaves notable gaps.

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?

One concise, front-loaded sentence with no filler. It could contain more useful detail, but what is included is efficient and to the point.

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 tool with four parameters, no annotations, and no output schema, the description is too thin. It fails to clarify how parameters affect behavior, what filtering is possible, or what the response contains. An agent would likely need to guess at important calling details.

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 only 25%, so the description should compensate. It does not explain limit, conversation_id, or working_directory meaning, relying only on parameter names. The description adds no real parameter-level value.

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: 'List recent user prompts sent to Warp / Oz.' It is clear what the tool returns, though it does not explicitly differentiate itself from siblings like list_conversations or search.

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?

No guidance on when to use list_queries versus alternatives such as search or list_conversations. There are no conditions, exclusions, or preferred use cases provided.

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