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 the search order (local first, then Professor Sausages catalog), what is returned (method, URL, price, how to call), and that it is free. It does not mention limitations like result caps or whether the tool executes the underlying request, but it is otherwise transparent.
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?
Two sentences, front-loaded with the main action, and every clause earns its place: usage instruction, examples, search scope, output content, and price. There is no fluff or repetition.
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 one-parameter discovery tool with no output schema, the description is complete. It explains the input format, the search behavior, the output contents, and cost, making it fully actionable for an agent deciding whether and how 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?
Schema coverage is 100% for the single 'task' parameter, so the baseline is 3. The description adds value beyond the schema by giving additional examples of plain-language queries (e.g., 'is this token a honeypot', 'read this page'), showing the parameter accepts questions and imperative phrasings, not just data requests.
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 identifies a discovery/search action ('Searches... returns matching endpoints') with a specific resource (data sources/endpoints). It distinguishes itself from sibling tools by showing it accepts plain-language requests and covers the whole catalog, unlike the specific screening tools listed as siblings.
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 usage context: describe what data you need in plain language, and it searches local then global catalogs. Examples like 'is this email deliverable' imply it can route to sibling tools like email_check, but it does not explicitly state when not to use this vs. direct alternatives.
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 targets a distinct resource type (crypto address, domain, email, IBAN, phone, URL, vessel, entity name) or a distinct purpose (pricing, data discovery, data request). Overlapping sanctions tools are clearly differentiated by target: address_screen for addresses, sanctions_screen for names, vessel_screen for vessels, and sanctions_entity for detailed records after screening.
Most data-check tools follow a consistent object_verb pattern (e.g., address_screen, email_check, phone_check). However, find_data and request_data invert the order, domain_intel uses a noun instead of a verb, and pricing stands alone as a gerund, creating mixed conventions.
12 tools is well within the ideal range for a data-screening server. Each tool covers a distinct verification task, and the additional meta tools (pricing, find_data, request_data) are useful entry points without bloating the core purpose.
The server covers a comprehensive set of screening and validation tasks across sanctions, domain, email, phone, IBAN, and URL. It includes a discovery tool (find_data) and a suggestion tool (request_data) to fill gaps, though an IP checker or company registry lookup could be considered minor omissions.