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

Goes well beyond the readOnly/openWorld annotations: discloses the read strategy (JSON-LD first, then tap-to-call links and page text), that supplied facts win and are required for sites blocking automated readers, and the normalization rules with concrete examples. It also describes the per-field verdict model (consistent/variant/mismatch) and that fixes are returned.

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?

Front-loads the comparison action and keeps to one dense paragraph where each sentence adds a distinct behavior (sources, input strategy, normalization, output, override). It is a little packed and some ideas are compressed into comma-chained clauses, but little is wasted.

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 nested-object tool with no output schema, the description covers input strategy, normalization, verdict vocabulary and that exact fixes are returned, which is enough to call it correctly. Minor gaps remain around ordering, limits (the 8-source cap is only implied by 'up to 8'), and how conflicting supplied facts are reported.

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 only 50%, so the description must compensate, and it does: it clarifies that each profile may carry a url, supplied facts, or both, that supplied facts override scraped values, and what the optional business object does (canonical override instead of majority). Format normalization examples further clarify acceptable input shapes, though the individual address field mappings are left to the schema.

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 verb and resource with scope: compare name, phone, street, city, region and postal code across up to 8 named source types. It is clearly distinguishable from siblings like validate_structured_data or generate_local_business_schema, which handle single-page validation or generation rather than cross-source comparison.

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 mechanics ('For each source give a url... and/or the facts shown on it') and it does give one conditional: supply business to compare against a canonical record instead of the majority. However, it never states when to prefer this tool over the sibling audit/validate tools, so the agent must infer the routing.

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