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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: the fallback mechanism (Stratalize composite medians when no vendor-specific row exists), the source (healthcare_vendor_benchmarks), and the pricing model ($0.10 USDC per call). It also explains the interpretation of the output (above monthly_high means overpaying). This goes beyond what annotations provide.

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 well-structured and front-loaded: it states the use case first, then the output fields, then the example, then the source and pricing. Every sentence earns its place. The example is concrete and illustrative without being verbose. The pricing information is useful for an agent deciding whether to invoke the tool.

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 read-only lookup tool with 100% schema coverage and no output schema, the description is quite complete. It covers the use case, output fields, fallback behavior, source, and cost. The only minor gap is that it doesn't describe the format of the output values (e.g., currency formatting, date format for data_as_of), but this is a minor omission given the tool's simplicity.

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 all three parameters (vendor_name, industry, company_size). The description adds context about the vendor_name parameter through the Salesforce example, but doesn't add meaning beyond the schema for industry or company_size. The baseline of 3 is appropriate since the schema does the heavy lifting and the description doesn't introduce new parameter-level semantics.

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 states the tool's purpose: checking if a procurement team is overpaying for a software vendor. It specifies the exact resource (healthcare vendor benchmark lookups), the output fields (monthly_median, monthly_low, monthly_high, annual_median, pricing_model, source, data_as_of), and the fallback behavior (Stratalize composite medians). This distinguishes it from siblings like get_vendor_benchmark and get_healthcare_vendor_market_rate by focusing on healthcare vendor pricing with a specific overpaying use case.

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 provides clear context for when to use this tool: when a CFO or procurement team needs to know if they are overpaying for a software vendor. It gives a concrete example (Salesforce CRM median ~$8,400/mo) and explains the fallback behavior. However, it doesn't explicitly name alternative tools or state when NOT to use this tool versus siblings like get_vendor_benchmark or get_healthcare_vendor_market_rate, so it misses the explicit exclusion criterion.

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