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malkreide

global-education-mcp

by malkreide

oecd_search_datasets

Read-onlyIdempotent

Search OECD datasets for education and other topics using a keyword. Retrieve dataflow IDs and dataset details from the OECD SDMX API.

Instructions

Durchsucht alle OECD-Datensätze nach einem Stichwort.

Über die OECD SDMX API sind hunderte Datensätze verfügbar. Diese Funktion findet Datensätze mit Bildungsbezug oder anderen Themen.

Args: params: keyword, limit

Returns: Markdown-Liste gefundener Datensätze mit Dataflow-IDs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds value by noting the API source ('OECD SDMX API'), that it searches hundreds of datasets, and that the output is a Markdown list with Dataflow-IDs. No contradictions with annotations.

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?

The description is well-structured with a lead sentence stating the purpose, followed by context, then an Args/Returns section. It is concise and contains no superfluous information, earning a high score.

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's moderate complexity (search with two parameters, output as list), the description covers purpose, data source, and output format. An output schema exists, so explaining return values is unnecessary. Missing details like error handling or empty results are minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description barely adds value beyond the input schema: it only lists parameter names ('keyword, limit') without describing their purpose or constraints. The schema itself already contains good descriptions for each parameter, so the description's contribution is minimal, leading to a low score.

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 'Durchsucht alle OECD-Datensätze nach einem Stichwort', identifying the verb (search) and resource (all OECD datasets) with a specific action (by keyword). This distinguishes it from siblings like oecd_list_education_datasets, which likely lists all without searching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context about the SDMX API and that it finds datasets related to education or other topics, but it does not explicitly state when to use this tool versus alternatives. There is no mention of prerequisites or when not to use it, leaving differentiation to the agent's inference.

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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