agent_llm_request

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

With no annotations, the description carries the full burden of behavioral disclosure. It does not mention that the tool likely uses an API key, consumes tokens, stores conversation history via session_id, or potentially calls external services. The one-sentence description reveals nothing about side effects or requirements beyond the obvious.

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 description is a single, front-loaded sentence with no fluff. It earns its place by stating the fundamental action clearly. However, it is so brief that it borders on under-specification, but conciseness itself is strong.

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 tool has 9 parameters and no annotations, making it a complex tool. The description only states the basic function and omits explanations of optional parameters (base_url, provider, temperature, etc.) or how session_id affects behavior. While an output schema exists, it does not compensate for the missing context needed to use the tool correctly.

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% and the description adds no parameter explanations. The 9 parameters (model, prompt, base_url, provider, max_tokens, session_id, api_key_env, temperature, system_prompt) are not described in the text. Even though property names hint at meaning, the description provides zero added value for understanding parameter semantics.

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 a specific action: 'Make a request to an OpenAI-compatible LLM provider.' It identifies the verb (make a request) and the resource (OpenAI-compatible LLM provider), which is distinct enough from sibling tools like router_chat or judge_code_change. It could be more specific about session handling, but the core purpose is evident.

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 gives no guidance on when to use this tool versus alternatives. Sibling tools include router_chat, start_session, and various judge_* tools, but no comparative context is provided. An agent is left to infer when a direct LLM request is appropriate.

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