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

With no annotations provided, the description carries the full burden, and it excels: it discloses the training-data limitation ('figures are estimates'), the role-level scope (not a named person), the pay-per-call cost ($0.06 USDC on Base via x402), and the exact failure mode when payment header is missing (error whose data carries payment terms). No annotation contradiction exists.

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?

Four sentences with zero waste: purpose and deliverables, usage routing, data-limitation caveat, and payment/auth behavior. Each sentence adds essential information that is not available elsewhere, and the critical scope/usage info is front-loaded.

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 2-parameter tool with no output schema and no annotations, the description fully covers what an agent needs to decide to call it, how to sequence it, what limits to expect, what it costs, and how payment errors surface. The listed deliverables also effectively describe the expected output shape.

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 100%, so the schema already documents both parameters with examples and purpose. The tool description adds no parameter-specific meaning beyond what the schema provides; it only ties context to the outreach angle conceptually. This is the baseline-3 situation where the schema does the heavy lifting.

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 states a specific verb and resource: 'Research a sales prospect company,' and enumerates concrete deliverables (company profile, decision-maker role profile, likely pain points, outreach angle). It clearly distinguishes itself from siblings like generate-cold-email by framing it as pre-outreach research, and from extract-pain-points by covering the full prospect research scope rather than one isolated output.

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 says 'Use before writing outreach; then use generate-cold-email,' giving both a temporal placement and a named sibling alternative. It also states the tool is 'Based on model training knowledge, not a live lookup,' which implicitly tells the agent when not to use this tool (when live data is needed). This is clear when-to-use guidance with an explicit exclusion.

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