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, the description carries the full burden. It discloses search scope, return contents (method, URL, price, call instructions), and cost ('Free'). It does not mention side effects, but as a search tool, none are apparent.
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, front-loaded with 'Describe the data you need in plain language' and followed by concise behavioral details. Every sentence earns its place without fluff.
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 single-parameter discovery tool with no output schema and no annotations, the description covers purpose, process, return content, and cost. It is sufficient for an agent to select and invoke the tool correctly.
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% with a description for the task parameter. The description adds value by providing explicit plain-language examples and clarifying that the input is the user's own words, reinforcing the schema's meaning.
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 that the tool searches for data sources/endpoints matching a plain-language description and returns matching endpoints with method, URL, price, and call instructions. This specific verb (searches) and resource (datasets/catalog endpoints) distinguishes it from sibling tools like read_url or request_data.
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 examples of appropriate use (e.g., 'Apple risk factors 2023') and explains the search order (server first, then catalog). However, it does not explicitly state when not to use this tool or name alternatives, though the examples imply a discovery use case.
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 clearly distinct function—data discovery, holidays, icons, OCR, news, PDF extraction, pricing, URL reading, data requests, screenshots, timezone, and weather. There is no overlap in purpose; even read_url and screenshot_url differ by output format (markdown vs PNG).
All tool names use lowercase snake_case and are readable, but the pattern is not uniformly verb_noun—some are nouns (holidays, news, pricing, timezone, weather) while others are verb_noun (find_data, read_url). This is a minor inconsistency that doesn't impede predictability.
With 12 tools, the server sits comfortably within the ideal 3–15 range. Each tool earns its place covering a distinct web utility, and the count matches the broad but well-defined scope of a general-purpose web toolbox.
The tool surface is fairly complete for a general-purpose web utility server, covering common tasks like fetching, extracting, searching, and checking time/weather/holidays, plus meta tools (pricing, request_data). Minor gaps like a generic text summarizer or video tool exist, but nothing critical for the intended purpose.