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

With no annotations, the description fully carries the behavioral transparency burden. It discloses that a human sends a call link within one business day, that the demo is built on the user's actual market rather than a generic sample, and that the call is free and nothing is charged. It also instructs the agent to confirm details with the user before calling, which is important behavioral guidance.

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 front-loaded with the core purpose and includes targeted details about process, timing, target users, and cost. There is minor redundancy in saying 'free demo call' and then 'Free, nothing charged,' and the second sentence is somewhat long, but every other sentence contributes a distinct fact.

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 7-parameter tool with no output schema and no annotations, the description covers the purpose, process, timing, cost, eligibility, and the key kind distinction. Small gaps remain around the optional contact_name parameter and what exactly the agent receives immediately after making the request, but overall the description is sufficient for correct invocation.

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 high at 86%, so the schema already documents most parameters. The description adds useful context around the 'market' parameter (real engines, real buyer questions) and reinforces the 'kind' distinction between custom reports and agency white-label demos, but it does not add meaning for optional fields like contact_name or roster_size beyond what the schema provides.

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 names the specific action (Request) and resource (a free demo call with Saymetry), and explains that it produces a custom demo report built on the user's actual market. This clearly separates it from sibling tools like check_ai_readability or get_offer_sheet, which involve different deliverables.

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 explicitly states when to use the tool: for marketers or business owners wanting to know what AI says about their business, needing a custom AI-visibility report, or for agencies evaluating a white-label dashboard. It gives a clear use context and adds the practical instruction to confirm details with the user, though it doesn't name alternatives or state when not to use the tool.

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