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

Annotations already establish readOnly/non-destructive/local behavior, and the description adds real behavioral context: a deal-size range is refused, omitted industry/sector/business model are inferred from other inputs and explicitly named back, and the tool stays neutral when inputs are unclear. It stops short of describing output volume or limits, but goes well beyond the annotation baseline.

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 dense sentences that lead with inputs and the returned artifact, then the conditional variations. Efficient and front-loaded, though the second sentence is long and packs several distinct behaviors together.

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?

No output schema exists, so the description carries the return-value burden and does so by naming tiers, commissions and KPIs. With 10 fully-described parameters and safe annotations, the only missing pieces are optional-input fallback details, which are partly covered.

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 schema baseline is 3. The description adds meaning beyond it by explaining downstream effects: partner_goals becomes the target of the first KPI row, your_deal_size scales example commission amounts while rates stay fixed, and industry/your_sector drive sector-specific buyers and objections.

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 transformation: takes company, product, partner model, goals and deal size, and returns a partner program with tiers, example commissions and KPIs. It also names the six partner-model variants it structures differently, so an agent can distinguish it from generic GTM siblings like launch_commander or craft_gtm_analyzer.

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?

Usage is implied by the required input set (you need a partner model and deal size, so this is a partner-program design task), and it explains that structure varies by partner_model. However it never states when to pick this over sibling GTM tools or any exclusions, leaving routing to inference.

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