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Generate pricing.md

pricing_generate
Read-onlyIdempotent

Generate a machine-readable /pricing.md for AI agents by extracting pricing tiers and prices from any SaaS or e-commerce site.

Instructions

Generate a machine-readable /pricing.md for AI shopping/agent flows. Finds the site's pricing page (or uses pricing_url), extracts named tiers and price lines, and returns the file content as a string.

Read-only. Issues a few HTTP GETs probing common pricing paths. Deterministic; no LLM. Does NOT write or upload — the caller hosts the file at https://<domain>/pricing.md.

When to use: a SaaS/e-commerce site that wants agents to read pricing without parsing a JS-rendered table. Falls back to a fill-in template when no prices are detectable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesHostname or origin to generate pricing.md for, e.g. `example.com`. The tool finds the pricing page (or uses `pricing_url`), extracts tiers and prices, and returns a machine-readable pricing.md string. Read-only.
pricing_urlNoExplicit pricing page URL. If omitted, the tool probes common paths (/pricing, /plans, /pricing/).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYes
pricing_mdYesThe generated pricing.md content. Caller hosts it at /pricing.md.
source_urlYesThe pricing page the content was derived from (null when none was found).
suggested_pathYes
tiers_detectedYesNumber of pricing tiers extracted.
validation_issuesYesIssues encountered while deriving the file.
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: read-only, HTTP GETs, deterministic, no LLM, no write/upload. This is valuable context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each purposeful: purpose, behavior details, usage context. No wasted words, well-structured, and front-loaded with primary purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two parameters and clear annotations, description is complete. Covers what, how, when, and fallback. Output schema exists but not shown; description clarifies return format.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. Description adds meaning by explaining automatic detection of pricing page, return type (string), and fallback template. Provides significant added value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool generates a machine-readable /pricing.md file for AI shopping/agent flows. It specifies the verb 'generate', resource 'pricing.md', and distinguishes from sibling audit/score tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: SaaS/e-commerce sites needing agent-readable pricing. Mentions fallback behavior when no prices are detectable. Could improve by naming alternative tools, but overall clear context.

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

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