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Yugalinks Commerce Intelligence

search_trade_datasets

Read-onlyIdempotent

Search selected approved commerce datasets without exposing storage details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoOptional text filter.
yearNoReporting year from 1995 through 2024.
limitNoMaximum rows returned for each selected dataset.
scopeNoOptional text filter.
offsetNoNumber of rows to skip.
hs_codeNo
country_iso3No
dataset_keysYesOne to eight dataset keys returned by list_trade_datasets.
exporter_iso3No
importer_iso3No

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already cover readOnlyHint, idempotentHint, and destructiveHint, so the description does not need to restate safety. The phrase 'without exposing storage details' adds a small behavioral guarantee, but the description offers little else about behavior such as pagination, filtering semantics, or result shape beyond what the schema encodes.

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

Conciseness5/5

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

A single sentence carries the core purpose without filler. The main action and scope are front-loaded, and every word earns its place.

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 rich input schema, robust annotations, and an output schema, the description is mostly sufficient: an agent can infer required inputs and safe read-only behavior. It is slightly incomplete because it does not explain when to use this tool over its siblings or clarify what makes datasets 'approved,' but the schema compensates for key invocation details.

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

Parameters3/5

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

The description itself adds no parameter-level meaning, and schema description coverage is only 60%, so it does not reach the higher baseline. However, the input schema provides useful per-parameter descriptions, including dataset_keys being returned by list_trade_datasets and the constraints on year, hs_code, and country codes.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly names the action ('Search') and the resource ('selected approved commerce datasets'), so an agent can tell this is a multi-dataset search tool. It does not explicitly distinguish itself from the sibling get_* and list_trade_datasets tools, which keeps it from a 5.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool versus alternatives like list_trade_datasets or the get_* trade tools. The phrase 'selected approved commerce datasets' implies a restricted scope, but no explicit when/when-not conditions or sibling references are provided.

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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