list_models

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

With no annotations, the description carries the burden of behavioral disclosure. It transparently explains the priority order: first try the provider's /v1/models endpoint, then fall back to local database models. This is valuable context beyond the simple 'list' operation. It does not mention side effects (none expected) or error handling, but the fallback logic is a key behavioral trait well disclosed.

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 long, front-loaded with the primary purpose, followed by a concise behavioral note. Every word earns its place; no fluff or repetition.

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 the tool has only one parameter and an output schema (present but not shown), the description covers the main behavior and the fallback logic. It does not mention error cases or return format, but the output schema likely covers those. For a low-complexity list tool, this is reasonably complete.

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 references the provider's /v1/models endpoint, implying provider_id identifies a specific provider with that endpoint. However, it does not explicitly explain the provider_id format or that it should come from a known provider list. The parameter name itself is self-explanatory, but the description adds only marginal semantics beyond the 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 clearly states the tool lists models available for a given LLM provider, using the specific verb '列出' (list) and resource '某 LLM 供应商可用的模型'. It distinguishes itself from sibling tools like list_providers (which lists providers) and add_model (which adds models).

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 provides clear context: use this tool to retrieve models for a provider. It does not explicitly name alternatives, but the fallback behavior (real-time API vs. local DB) gives the agent an understanding of what to expect. No exclusions are stated, but the use case is clear from the description and sibling tool names.

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