Fetch a public business website page and return structured, accessible facts for a buyer-readiness review. It only examines the supplied public URL and does not scrape review platforms or private systems.
| Name | Required | Description | Default |
|---|---|---|---|
| website_url | Yes | A public HTTP or HTTPS page URL to evaluate. | |
| target_service | No | Optional service the buyer is researching, such as pool renovation or roof replacement. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for disclosing behavior. It explicitly states what the tool does (fetches and returns facts) and what it does not do (does not scrape review platforms or private systems). This transparency is sufficient for users to understand the tool's scope and limitations.
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 concise, consisting of two sentences that contain all essential information without redundancy. It is well-structured, presenting the main action and then clarifying constraints and exclusions.
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?
The description is adequate for a simple fetch tool, covering purpose, scope, and parameters. It could be slightly more explicit about the format of the returned 'structured facts,' but given the lack of an output schema and the tool's simplicity, the current description is sufficiently complete for most users.
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?
The schema provides clear descriptions for both parameters: website_url is a public HTTP/HTTPS URL to evaluate, and target_service is an optional service the buyer is researching (e.g., pool renovation). These descriptions align with the tool's purpose, and the schema coverage is 100%, so no additional explanation is needed.
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 function: fetching a public business website page and returning structured facts for buyer-readiness review. It also distinguishes itself from sibling tools by explicitly noting it does not scrape review platforms or private systems, making its purpose unambiguous.
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 usage context by specifying it is for buyer-readiness review and that it only examines the supplied public URL. This implies when to use this tool (for inspecting websites) versus others (like compare or score), but it does not explicitly list alternative scenarios. However, the boundaries are clear enough.
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.
Most tools have clearly distinct roles: inspect_website gathers facts, score_website_evaluability produces a rubric score, organize_review_evidence handles review signals, compare_shortlist_candidates merges scores, and generate_paid_scope produces a brief. The only mild ambiguity is between inspect_website and score_website_evaluability, since both involve website inspection, though their outputs differ.
All tool names follow a consistent verb_noun pattern: compare, generate, inspect, organize, and score are clear actions with descriptive objects. The naming is predictable and makes the tool set easy to navigate.
Five tools is well-scoped for the server's niche buyer-readiness purpose. Each tool contributes to a focused workflow without redundancy or unnecessary sprawl.
The workflow covers website inspection, website scoring, review evidence organization, comparison, and downstream brief generation. However, compare_shortlist_candidates expects a review trust score, and no tool actually computes that score from organized review evidence, leaving a notable gap in the core shortlisting flow.