chat

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 explains the internal pipeline (retrieve chunks, then summarise), and the critical fallback behavior ('without them the answer is based on LLM knowledge only'). It does not disclose the return format or potential side effects, but the described process and fallback are meaningful behavioral traits that go beyond mere purpose.

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 three sentences of dense, valuable information. It front-loads the core purpose, then adds critical usage guidance and a sibling alternative. There is zero fluff; every sentence earns its place.

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 four parameters, no annotations, and no output schema, the description covers the essential ground: purpose, usage, and key parameter insights. It lacks an explicit statement about return values or error conditions, but the tool is a chat interface and the described behavior is sufficiently complete for an agent to select and invoke it correctly. It earns a 4 rather than a 5 due to the absence of any return-format hint or mention of web_search_enabled behavior.

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%, so the baseline is 3. The description adds significant semantic value for knowledge_base_ids by explaining why to provide them, the consequences of not providing them, and pointing to list_knowledge_bases for discovery. It does not add detail for web_search_enabled, but the schema already covers it; the added emphasis and practical context for the key parameter justify a 4.

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 opens with a clear, specific verb-resource pair: 'RAG pipeline chat: retrieve relevant chunks from knowledge bases, then summarise with LLM.' It explicitly distinguishes itself from sibling tools by naming agent_chat for multi-step reasoning, and list_knowledge_bases for discovery, making the tool's unique role unmistakable.

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?

Provides explicit usage guidance: 'ALWAYS provide knowledge_base_ids' and explains the alternative 'For multi-step reasoning or tool-calling use agent_chat instead.' It also tells the agent to use list_knowledge_bases to discover available bases, giving both when-to-use and when-not-to-use context.

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