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 does disclose that the answer is 'short', 'grounded in Relendi's knowledge base', and includes 'citations' – useful response-format information. However, it does not discuss caveats like answer precision, the possibility of no answer, or the relationship to knowledge-base search, leaving some behavioral ambiguity for a no-annotation tool.

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?

Two sentences, no waste. The primary purpose is front-loaded, and the routing guidance to exact alternatives is placed second. Every clause 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?

The description covers the core call flow and gives a partial return description ('short answer ... with citations'). However, with no output schema and no annotations, it leaves unaddressed how this tool differs from the sibling search_cre_knowledge, and it does not state whether the answer is generated or retrieved. An agent might be uncertain which of the two knowledge-related tools to pick.

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 0%, so the description must compensate. It does clarify that the 'question' parameter should be plain-English and on the topic of commercial real estate lending, adding domain context beyond the bare schema name. But it stops short of giving examples, question-quality guidance, or expected phrasing, so compensation is only partial.

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?

States a specific verb ('ask'), a concrete resource ('Relendi's knowledge base'), and the expected output ('short answer ... with citations'). It also distinguishes itself from the exact-math siblings size_loan and run_scenario, so an agent can tell what this tool is for at a glance.

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?

Explicitly tells the agent when not to use this tool: 'For loan math prefer size_loan or run_scenario, which are exact.' This is a clear when-not statement with named alternatives. It implies the appropriate use for plain-English conceptual questions, giving the agent the decision rule it needs.

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