AI Chat (RAG over chart)

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

The description adds behavioral context beyond annotations: it is a chat (multi-turn via history) and persona-tunable. Annotations already indicate read-only, non-destructive, non-idempotent, and open-world behavior. The description aligns with these without contradiction.

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 very concise with two sentences and an example request. It front-loads the core purpose. However, structural improvements could include a list of key parameters.

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 absence of an output schema, the description should hint at the response format (LLM string). It mentions 'LLM chat' implying natural language output, but doesn't state that explicitly. The example only shows request. No mention of the 'history' parameter's role in multi-turn conversations.

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 low (25%) and the description only provides a single example request body. It does not explain the 'chart' object fields, 'history' structure, or 'language' options. The schema itself contains rich nested properties, but the description fails to compensate for the low coverage.

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 title 'AI Chat (RAG over chart)' and description 'LLM chat grounded in a natal chart context. Persona-tunable.' clearly state the tool's purpose as a conversational LLM interface for natal charts. It distinguishes from siblings like 'interpret_natal' by specifying chat and RAG capabilities.

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?

No explicit guidance on when to use this tool versus the many sibling interpretation tools. The description does not mention typical use cases, prerequisites, or when to prefer alternatives. The agent is left to infer usage from the title.

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