Agent requests an LLM SEO audit on behalf of its buyer. Routes to the right tier (self-serve vs. done-for-you vs. retainer) and returns a confirmation with checkout or booking links.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | ||
| urgency | No | ||
| company_name | Yes | ||
| contact_email | Yes | ||
| tier_interest | No | ||
| buying_context | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool routes to tiers and returns confirmation links, but lacks details on side effects (e.g., does it trigger emails or create tickets) or authentication requirements.
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 two concise sentences that efficiently convey the tool's action, routing logic, and output. No extraneous information is present, earning every sentence its place.
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?
Given six parameters (two required) and no output schema, the description covers overall purpose and outcome but lacks parameter explanations and usage context. It is minimally adequate but leaves gaps for agent understanding.
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 0%, meaning the description adds no meaning to the six parameters. It does not explain what company_name, contact_email, domain, urgency, tier_interest, or buying_context represent or how they affect behavior.
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: requesting an LLM SEO audit on behalf of a buyer, with routing to different tiers and returning confirmation links. This distinguishes it from sibling tools like get_offerings or assess_fit, which are information retrieval or analysis tools.
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 implies usage when a buyer needs an audit but does not explicitly state when to use or not use this tool, nor does it mention alternatives. There is no guidance on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct function: assessing fit, listing surfaces, offering details, proof points, transparency metrics, and requesting audits. No overlap in purpose.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., assess_fit, get_offerings, request_audit), making them predictable and easy to differentiate.
With 6 tools, the server is well-scoped for its domain of LLM SEO services, covering all key interactions without being bloated or sparse.
The tool surface covers prospecting, information retrieval, and action (request_audit), but lacks a mechanism for checking audit status or cancellation, which is a minor gap.