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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds genuinely useful behavior beyond them: the model is local, reasoning tokens are consumed but 'not returned', and finish_reason 'length' signals truncation. It stops short of describing concurrency, timeout, or error behavior, so 4 rather than 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?

The core action is front-loaded in one sentence, then parameters are listed one per line with no filler. Every clause adds information (defaults, trade-offs, truncation semantics) rather than restating the schema.

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?

With an output schema present, the description need not enumerate return fields, and it correctly focuses on the one return nuance an agent must act on (finish_reason 'length'). Nothing critical is missing for a 5-parameter inference call, though latency/cost hints beyond the 'think' note would round it out.

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 carries the full burden and does so: it explains system, model (source and default), think (behavioral trade-off plus the fact that reasoning is not returned), and max_tokens ('reasoning included' plus the length cutoff meaning). This is meaning the bare schema types and defaults cannot convey.

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?

States a specific verb and resource ('Send one prompt to a local model and return its answer text'), and the parameter note 'a name from list_models' implicitly establishes the division of labor with its only sibling. An agent can tell immediately that this performs inference while list_models enumerates.

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 clear operational guidance for the optional parameters, e.g. 'omit for the default' and 'think: better on hard problems, but often several times slower'. It does not state exclusions or explicitly contrast with list_models, but the usage context is unambiguous for a single-prompt inference call.

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