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

The description adds meaningful behavioral detail beyond the readOnly/idempotent annotations: it states the model must research category multiples fresh and date-stamp them, explicitly rejects hardcoded tables, and specifies that the red-flag checklist runs before financials are shared and seller questions are built from research gaps. This gives the agent a clear picture of the tool's internal process and constraints.

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 dense but well-organized: a lead sentence enumerating the four framework components followed by four example invocations. The examples earn their place by clarifying acceptable request phrasings, though the opening sentence is heavy with parentheticals and could be tightened without losing meaning.

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?

For a tool of this complexity, the description covers purpose, deliverables, behavioral constraints, and practical invocation examples, while the output schema handles return-value expectations. An agent has everything it needs to decide whether to call this tool and how to phrase the request correctly.

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?

The input schema already provides 100% coverage for all four parameters, so the baseline is 3. The description adds illustrative context by showing concrete city_metro formats ('Minneapolis, MN', 'Wichita, KS'), asking_price examples ('$650K', '$310K'), and how category and asking_price are used for sanity-checking against multiples, which helps an agent formulate effective calls.

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 identifies a pre-diligence framework with four concrete deliverables: SDE framing, a category multiple range, a red-flag checklist, and a prioritized seller-question list. The example invocations show exactly what the tool does, making it easy to distinguish from siblings like business_teardown or market_opportunity_scan.

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 example invocations establish clear usage contexts: preparing for diligence on a specific business, asking seller questions before an offer, checking red flags before taking an asking price seriously, and sanity-checking against category multiples before meeting the seller. It does not explicitly name sibling alternatives or state when not to use the tool, so it falls just short of full routing guidance.

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