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search_datasets

Read-only

Find datasets on DataBazaar by natural-language query with category, price, quality, and pricing filters; get ranked listings with prices, quality scores, and checkout URLs.

Instructions

Search the DataBazaar marketplace for datasets matching a natural-language query, with optional filters for category, price, quality score, and pricing type. Returns ranked listings with prices, quality scores, and checkout URLs. When results are empty, log_data_gap can post a bounty to signal demand to sellers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYesNatural language search query describing the data needed
categoryNoFilter by category: geographic, pricing, sensor, images, text, financial, scientific, social, retail, other
max_priceNoMaximum price in USD — omit to include all prices
pricing_typeNo
min_quality_scoreNoMinimum assessed sample structure score (0-100); unassessed listings are excluded. See https://databazaar.io/quality for the rubric and limitations.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.1

TDQS

A4/5.0
Behavior4/5

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

Annotations already carry readOnlyHint=true, so the safety profile is covered. The description adds meaningful behavior beyond that: it discloses the return shape (ranked listings with prices, quality scores, checkout URLs) and the empty-result path. Rate limits and auth requirements are not mentioned, but the safety-relevant context is adequate.

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?

Three tight sentences, front-loaded with the core purpose and filters, followed by return shape and the empty-result fallback. Every sentence earns its place with no redundancy.

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?

With no output schema, the description compensates by describing the return values (ranked listings, prices, quality scores, checkout URLs) and the empty-result behavior. It is nearly complete for a 6-param search tool, missing only pagination/limit semantics now that limit is not referenced.

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?

Schema description coverage is 67%, and the description conceptually groups the filters (category, price, quality score, pricing type) but adds no syntax, format, or edge-case detail beyond the schema. With the schema doing most of the heavy lifting and one filter (limit) unmentioned, this is a baseline-adequate 3.

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?

Names a specific verb (search) and resource (datasets) within a named marketplace (DataBazaar), and enumerates the filter dimensions, so the tool's job is unambiguous. However, it does not explicitly distinguish itself from siblings get_dataset and preview_sample, leaving the agent to infer that this is the discovery step.

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?

Gives clear context for the tool's role (natural-language discovery over the marketplace) and adds a useful fallback: when results are empty, log_data_gap can signal demand. It stops short of explicitly stating when to prefer this over get_dataset or preview_sample, so no full when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.