List Models

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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint, so the safety profile is covered. The description adds non-obvious context the annotations cannot convey: the auth/token requirement (no PAT, internal token needed) and what the response contains. No pagination or rate-limit detail, hence not a 5.

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?

Three tight sentences with no filler, front-loaded with what it does, then how to use the result, then the auth caveat. 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 zero-param, read-only listing tool with an output schema and full annotation coverage, the description covers the remaining gaps: consumer tools, returned field meaning, and the auth constraint. Nothing an agent needs to call it correctly is missing.

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 tool takes zero parameters, so per the rubric the baseline is 4. The description instead clarifies the output field semantics (name vs display name vs capability flags), which is the only meaningful 'argument' concept here.

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?

Specific verb+resource ('List the models configured on the DeerFlow instance') plus the returned fields (name, display name, capability flags). It distinguishes itself from siblings by naming deerflow_research/deerflow_chat and explaining the relationship (this tool supplies their model argument).

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?

Explicitly states the use case — resolve a model name to pass into deerflow_research or deerflow_chat — and adds a hard prerequisite caveat that this endpoint is not in the PAT route allowlist and needs an internal token. Both when-to-use and a blocking condition are given.

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