config_models_show

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

With no annotations provided, the description carries full burden. It states 'Takes no arguments. Read-only. Output is YAML.' and explains that it returns the embedded catalog only, not merged overrides. No contradictions.

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 somewhat long but every sentence adds value. It could be slightly more concise, but it's well-structured and front-loaded with the core purpose.

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?

Given no parameters and no output schema, the description explains output content (model identifiers, token limits, provider) and format (YAML). It also distinguishes from the plain `show` command. Complete for a simple listing tool.

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?

There are zero parameters, so the description adds value by explicitly stating 'Takes no arguments.' Schema coverage is 100% (empty schema). Baseline for 0 params is 4, and the description meets it clearly.

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 clearly specifies the tool returns the embedded `models.yaml` listing every supported AI model with identifiers, token limits, and provider. It distinguishes itself from siblings by stating its use for discovering valid `model` values for `ai_chat` and git tools.

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 explicitly states when to use: 'to discover the valid `model` values accepted by `ai_chat` and the git tools.' It also contrasts with the plain `show` command that merges user/project overrides, providing clear context and alternatives.

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