ask_ai_about_documents

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

With no annotations provided, the description carries full burden. It discloses that the tool returns 'AI-generated answers based on document content with sources', which is valuable behavioral context. However, it doesn't mention rate limits, authentication requirements, accuracy limitations, or whether this is a read-only operation (though implied by 'queries').

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 well-structured with clear sections (purpose, usage guidelines, args, returns) and every sentence adds value. It's front-loaded with the core purpose, followed by specific guidance, and avoids redundancy.

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 no annotations and no output schema, the description does a good job explaining what the tool does, when to use it, parameters, and return format. However, for an AI-powered query tool, it could benefit from more behavioral context about limitations or confidence scores in responses.

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. It provides clear semantic explanations for all three parameters: 'question' as the natural language query, 'collection_id' to limit search to a collection, and 'document_id' to limit to a specific document. This adds meaningful context beyond the bare schema.

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 with specific verbs ('queries document content using natural language questions') and distinguishes it from siblings like 'search_documents' or 'read_document' by emphasizing AI-powered question answering rather than keyword search or raw document retrieval.

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 usage guidelines with a bulleted list of when to use this tool ('Find specific information across multiple documents', 'Get direct answers to questions about document content', etc.) and includes concrete examples of questions to ask. It implicitly distinguishes from siblings by focusing on natural language Q&A rather than document management operations.

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