list_models

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

With no annotations, the description carries the behavioral burden; it meets it by stating a read-only listing behavior, optional query filtering, and the exact data returned. There are no side-effect expectations, and 'List' clearly signals a safe read operation, though it omits any authorization/availability caveats.

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?

Two sentences with no filler, front-loading the core purpose before the optional filter and a clear use directive. Every sentence earns its place.

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 one-optional-parameter listing tool, the description covers what is returned, the optional filter, and the recommended call sequence with ask_llm. No output schema exists, but the description supplies the relevant return fields.

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?

The schema already fully documents 'query' with a thorough description of matching behavior. The tool description adds workflow context but no new parameter semantics, so the baseline of 3 for 100% schema coverage applies.

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?

Starts with 'List available Devin models', giving a specific verb and resource, and enumerates exact output attributes (pricing input/cached/output, context window, cost tier). The closing guidance 'before calling ask_llm' differentiates it from the sibling inference tool.

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?

Gives an explicit use context: 'Use this to discover cheap models before calling ask_llm.' It tells an agent when to invoke it, though it does not enumerate cases when it should not be used or mention list_sessions.

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