agnes_chat

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

No annotations are provided, so the description carries the full burden. It covers streaming behavior (server-side consumed, full text returned), thinking mode, tool calling, and vision support. However, it lacks details on potential issues like error handling, destructive actions, rate limits, or cost implications, leaving gaps in transparency.

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 concise and structured: four sentences covering main capability, features, models, and vision. No redundant information, and key points are front-loaded.

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 complexity (15 parameters, no output schema), the description covers the core purpose and capabilities well. However, it does not explain the return format or output structure, which would be helpful for an AI agent invoking the tool. Still, the description is largely complete for the main functionality.

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 100% with descriptions for all 15 parameters. The description adds minor context (e.g., 1M-token context limit, vision via image_url), but most parameter semantics are already well-documented in the schema. Baseline of 3 is appropriate.

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 it is for text generation and reasoning, OpenAI-compatible chat completions, and lists capabilities including multi-turn conversation, system prompts, tool calling, streaming, thinking mode, and vision. It also specifies supported models and context lengths, distinguishing it from siblings like agnes_vision which provides a simpler interface for image understanding.

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 mentions vision is available but suggests using agnes_vision for a simpler interface, providing some guidance. However, it does not explicitly state when to use this tool vs. alternatives like agnes_image or agnes_video_* tools, nor does it provide when-not-to-use scenarios.

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