chat_completion

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly explains default behavior (model default, thinking mode enabled by default), how to override (thinking:{type:'disabled'}), and side effects (conversation_id persists context, clear_conversation resets state, include_raw_response returns full provider payload). It lacks some details like rate limits or error behavior, but for a chat tool, it covers the key behavioral traits well.

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 dense but efficient, covering all major usage points in three sentences. It front-loads the core purpose and then lists key option combinations without verbosity. Every sentence adds new information, and the structure flows logically from core usage to advanced options.

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 (23 params, nested objects, no output schema), the description is remarkably complete. It covers selection of input format, model, thinking mode, context persistence, and debugging. While it doesn't cover every parameter (e.g., stop, tools, response_format), those are standard OpenAI-compatible parameters that the agent can infer from the schema. The description addresses the highest-value decisions for the 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 description coverage is 0%, so the description must compensate for the 23 parameters. It does this excellently by explaining the most important parameters and their interplay: message vs messages, thinking, reasoning_effort, conversation_id, clear_conversation, and include_raw_response. It also clarifies model default choices. This is critical guidance that the raw schema does not provide, making the description highly valuable.

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 identifies this as the primary DeepSeek V4 chat tool for single-turn and multi-turn generation, with a specific verb ('chat'), resource ('DeepSeek V4'), and behavior. It also distinguishes itself from sibling tools by being the primary chat tool, while sibling names like 'completion' and 'create_response' suggest overlap.

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 provides explicit guidance on when to use this tool, including model selection ('defaults to deepseek-v4-flash; use deepseek-v4-pro for higher-capability reasoning'), message format selection ('Provide either message or messages; if both are provided, messages is used'), and optional behaviors like conversation persistence and raw response inclusion. It doesn't explicitly name sibling alternatives, but the guidance is clear and context-rich.

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