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

Adds real context beyond the readOnly/openWorld annotations: the qualification rule (needs both ad and SEO evidence), the caveat that public emails are unverified decision makers, and a $0.05 per qualified result cost. That pricing and evidence-gating is exactly the kind of disclosure annotations cannot carry. It stops short of saying anything about latency, result shape, or what happens when a store fails qualification.

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?

Dense but every clause carries information — criteria, caveat, price, alternatives, exclusions. The opening semicolon-chained sentence is somewhat awkward to parse, but the routing guidance is clearly front-loaded in the second half.

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?

An output schema exists, so return values need no explanation. With cost, qualification criteria, data caveats, exclusions, and sibling routing all present, an agent has everything needed to decide whether to call it and what to expect.

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% and both parameters are fully documented in the schema, so the baseline applies. The description only implies that 'domain' identifies the known store and adds nothing about format or the context parameter.

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 (qualify a store as a lead) plus the required evidence (ad activity and SEO/schema-gap signals), which is far more than a restatement of the name. An agent can distinguish this from the readiness auditor and the contacts extractor without opening any schema.

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?

Names two explicit alternatives with the condition that selects each: muse_agentic_store_readiness_auditor for an audit without lead qualification, extract_company_contacts for contacts only. It also scopes usage to 'a known store' and excludes outreach and conversion-loss measurement.

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