find_dataDescribe the data you need in plain language (e.g. 'Apple risk factors 2023', 'is this token a honeypot', 'is this email deliverable', 'read this page'). Searches this server's datasets first, then the whole Professor Sausages catalog, and returns matching endpoints with method, URL, price, and how to call them. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | What you're trying to find or do, in your own words |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It transparently discloses search scope, output contents (method, URL, price, how to call), and that it is free. It does not go into error handling or auth requirements, but for a read-only discovery tool the disclosure is solid.
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 sentences: the first instructs the user with varied examples, the second explains the search behavior and return value. Every sentence contributes value and is front-loaded with actionable guidance.
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?
For a simple single-parameter tool with no output schema, the description covers the essential aspects: how to query, what the tool does internally, and what fields are returned. Minor omissions like no-match behavior are present but not critical for a discovery tool.
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 coverage is 100%, so the task parameter is already described. The description adds extra examples and contextual guidance ('plain language'), reinforcing how to phrase queries beyond the schema's bare description, which earns above-baseline credit.
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 a specific verb ('Searches') and resource ('this server's datasets first, then the whole Professor Sausages catalog'), and distinguishes itself from sibling tools by focusing on discovery rather than direct data retrieval. The title also reinforces the purpose.
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 gives clear context for when to use it ('Describe the data you need in plain language') and explains the search order. It does not explicitly name alternative tools for exclusions, but the examples and output format ('returns matching endpoints') imply it is a discovery tool preceding endpoint calls.
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.