Returns a 0–100 fit score, reasoning, and recommended tier for a prospective B2B SaaS buyer. Uses company stage, industry, AI-feature shipping status, and platform-partnership signals.
| Name | Required | Description | Default |
|---|---|---|---|
| stage | No | ||
| domain | No | ||
| industry | No | Vertical: aec, fintech, healthtech, legaltech, devtools, general_b2b_saas | |
| company_name | Yes | ||
| ships_ai_features | No | ||
| platform_partnerships | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the tool as a calculator that returns data, implying it is non-destructive, but does not explicitly disclose side effects, authentication needs, or performance characteristics.
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?
Two sentences: first states output, second lists inputs. No wasted words, efficient and 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?
Given no output schema, the description sufficiently explains the return format (score, reasoning, tier). It covers core inputs and purpose. Could be enhanced by clarifying if all signals are required or how the score is computed.
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?
With only 17% schema description coverage, the description needed to compensate. It mentions four of six parameters (stage, industry, ships_ai_features, platform_partnerships) but does not explain their values or behavior beyond the schema's enum/description. The domain parameter is omitted entirely.
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 returns a fit score, reasoning, and tier for a B2B SaaS buyer, and lists the signals used. It is distinct from sibling tools which cover surfaces, offerings, proof points, etc.
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?
No explicit guidance on when to use versus alternatives. While the purpose is clear, the description does not state when not to use or suggest other tools for different scenarios.
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.