query_local_llm_stateful

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: statefulness, speed advantage, server-side context preservation, and the prerequisite of a previous response ID. However, it does not describe return format or failure behavior, but the main behavioral context is covered.

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 short sentences, front-loaded with the primary use case. Every sentence adds value, with no redundant or irrelevant content.

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?

The description defines the intended context (follow-up conversations) well and differentiates from siblings. However, it omits information about the return value or error cases, and with no output schema, the description is incomplete for fully understanding the tool's behavior beyond selection.

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 33%, but the description crucially clarifies the role of `previous_response_id` (the key parameter) and ties it to the tool's purpose. It does not elaborate on other parameters like temperature or max_tokens, which remain unexplained, leaving some compensation gap.

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's purpose: continuing an existing conversation using a `previous_response_id`. It uses the verb 'continuing' and implicitly contrasts with the sibling 'query_local_llm', making it specific and differentiated.

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?

Provides explicit usage guidance: 'Use this ONLY for continuing an existing conversation when you have a `previous_response_id`.' It also gives reasons why (faster, preserves context server-side), effectively telling when and when not to use it.

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