query_local_llm

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

With no annotations, the description discloses the stateless nature (starting new conversation) and supported features. It does not mention side effects or resource implications, but for a query tool this is acceptable. It could add more, but it provides useful behavioral context beyond the schema.

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, front-loaded with 'Standard', every clause earns its place. It is concise without sacrificing key usage guidance.

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's complexity (15 parameters) and the absence of annotations/output schema, the description is too brief. It lacks parameter details, error behavior, and output format expectations, leaving significant gaps for an agent.

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 coverage is only 13%, so the description must compensate heavily. It only implies parameters for Vision and JSON Schema but offers no explanation for the other 12 parameters (temperature, top_k, penalties, etc.). This is insufficient for a 15-parameter tool.

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 starts a new conversation or handles one-off tasks, distinguishing it from sibling query_local_llm_stateful. It also lists key capabilities (Vision, JSON Schema, Reasoning), making the purpose specific and unambiguous.

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 explicitly says when to use (starting new conversations, one-off tasks) and when not to use (follow-ups with a response ID), effectively guiding the agent to the stateful sibling. This provides clear usage context.

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