query_local_5090

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

With no annotations, the description carries the full disclosure burden and does supply meaningful context: local execution, a specific model, and a zero-cost cost model. However it omits operational traits such as whether the machine must be online, latency/timeout expectations, and output format, leaving notable gaps for an unannotated tool.

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?

A single front-loaded sentence that conveys action, target, model, and cost with no filler. Every clause 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 simple two-parameter tool with no output schema and no annotations, the definition covers the essentials but not return shape or runtime expectations. It is adequate to invoke but not fully 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 100% and both parameters (prompt, system_prompt) are fully documented in the schema. The description only restates the concept of a query/code task and adds no syntax, format, or constraint details beyond the schema, so the baseline of 3 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?

States a specific verb (Run) and resource (query or code task) and uniquely identifies the backend (local RTX 5090, qwen2.5-coder:32b). It implicitly contrasts with the cascade sibling via 'directly on the local', but never names cascade_llm, so sibling differentiation is only suggested rather than explicit.

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 '$0 token cost' framing hints at the condition that favors this tool over a paid/cascade path, but no explicit when-to-use, when-not-to-use, or named alternative (cascade_llm) is given. Usage is inferable rather than stated.

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