List AI Builder prompts / models

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not state whether this is a read-only operation, whether it requires an authenticated environment, what happens when no environment is specified, or whether the output is a table or JSON. The description adds only the ID-returning detail, which is useful but insufficient for a tool with zero annotation coverage.

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 a single sentence that front-loads the command name and core purpose, then adds the cross-tool relevance. It is compact and every phrase earns its place, though it could be slightly more structured by separating the purpose from the usage note.

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 list tool with no annotations and no output schema, the description is thin. It does not mention filtering behavior, default environment resolution, output format, or whether inactive models are included by default. The cross-reference to cs_add_tool is helpful, but an agent still lacks enough context to know what results to expect or how the optional parameters affect the call.

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 33%: 'environment' is documented in the schema, but 'search' and 'activeOnly' have no descriptions. The tool description does not compensate by explaining these parameters. However, the names are fairly self-explanatory, and the description does clarify that the returned IDs are meant for cs_add_tool type 'prompt', which adds meaning beyond the schema. Baseline 3 is appropriate.

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 ('list') and resource ('AI Builder models including custom prompts') and adds the useful detail that it returns IDs for use with cs_add_tool type 'prompt'. It is clear enough to distinguish from sibling list tools like cs_list_flows or cs_list_agents, though it does not explicitly name a sibling 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?

The description implies usage context: it is the way to discover AI Builder model/prompt IDs before adding a tool of type 'prompt'. It does not explicitly state when to use it versus alternatives, nor does it mention the optional 'search' and 'activeOnly' filters as selection aids. The connection to cs_add_tool gives some guidance, but exclusions or alternative conditions are absent.

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