Price list
pricingEvery endpoint this server fronts, with its exact per-call USD price (x402, USDC on Base) and a one-line summary, read live from the route table. Free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
pricingEvery endpoint this server fronts, with its exact per-call USD price (x402, USDC on Base) and a one-line summary, read live from the route table. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses that data is 'read live from the route table' (no caching) and that it is 'Free' (no cost). For a simple read-only list tool, this is sufficient behavioral context.
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 a single, dense sentence that front-loads the purpose ('Every endpoint...') and includes key details (price, x402, USDC on Base, live, free). No wasted words.
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 the simplicity (no params, no output schema), the description covers the essential context: what the tool returns (endpoints, prices, summaries), the data source, and cost. It could mention the response format (e.g., list vs. text) but that is not critical for an agent to invoke it.
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 tool has zero parameters, so the schema is empty. The description adds no parameter details because none exist. According to the rubric, no params gives a baseline score of 4, and the description does not need to compensate.
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 what the tool does: it lists every endpoint with its exact per-call USD price and a one-line summary, sourced live from the route table. This is specific and distinguishes the tool from sibling data-retrieval tools by focusing on pricing metadata.
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: use this tool to discover endpoint costs or get an overview of available endpoints. It notes 'Free' as a cost characteristic, but doesn't explicitly state when not to use it or name alternatives. Still, the context is clear enough for an agent to decide.
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 serves a distinct purpose: data search, holidays, icons, OCR, news, PDF extraction, pricing, URL reading, data requests, screenshots, timezone, and weather. No two tools have overlapping scopes, and even similar tools like read_url and screenshot_url are clearly differentiated by their output format.
Names are readable and descriptive but follow no single pattern: some are verb_noun (find_data, read_url, request_data), others are noun_verb (icon_search, screenshot_url, pdf_extract), and several are bare nouns (holidays, news, pricing, timezone, weather). This mixing is not chaotic, but it lacks a consistent convention.
Twelve tools is a well-scoped size for a server that fronts a collection of data endpoints and document utilities. Each tool earns its place, covering distinct utilities without redundancy or bloat.
The server covers its stated web-and-documents domain well: URL fetching, PDF extraction, OCR, screenshots, plus a variety of data queries and a pricing/request mechanism. Minor gaps exist (e.g., no document creation or editing tools), but for a read/compute-oriented server the surface is comprehensive.