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furlen_public_data

Fetch public datasets from World Bank, IMF, FRED, Eurostat, UN Comtrade, WHO, and company filings by describing the data in plain English. Returns rows, CSV string, and source provenance.

Instructions

Fetch a real public dataset by describing it in plain English — no CSV needed. Pulls from the World Bank, IMF WEO, FRED, Eurostat, UN Comtrade, WHO, and company filings (SEC EDGAR, ESEF, DART, EDINET). Returns the rows, a ready-to-use csv string you can pass straight to furlen_render, and provenance naming the exact indicator and source. If no connected source covers the subject it says so rather than guessing — a 422 with code 'subject_unsupported' means the data does not exist here, not that the request was malformed, so do not retry it reworded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesWhat you want, in plain English. E.g. "India GDP over the last 10 years", "compare US and China population", "Apple revenue over 6 years", "US healthy life expectancy", "India exports to the US".
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that unsupported subjects return a 422 with code 'subject_unsupported', meaning the data does not exist, and that it does not guess. It also mentions provenance.

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

Conciseness4/5

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

Two sentences, direct and front-loaded. It conveys essential information without fluff. Could be slightly more concise but is efficient.

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

Completeness4/5

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

Given the tool is simple (1 param, no output schema), the description adequately covers purpose, error handling, and output format. No major gaps.

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?

The only parameter 'prompt' has 100% schema coverage. The description adds value by specifying it should be in plain English and providing examples, which goes beyond the schema's description.

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?

The description clearly states it fetches real public datasets using plain English, lists specific sources (World Bank, IMF, etc.), and explains the return value includes rows, a csv string for rendering, and provenance. It distinguishes from sibling tools by mentioning the csv string for furlen_render.

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?

It tells when to use (fetching public data) and how to handle a 422 error (do not retry). It doesn't explicitly state when not to use, but the context of siblings (rendering tools) implies this is for data retrieval.

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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