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VisionLibra

Search Physical AI datasets and data services

search_datasets
Read-only

Search VisionLibra's off-the-shelf egocentric (first-person) task datasets for robot learning and the data services: custom egocentric data collection, annotation (incl. RGB-D / 3D), robot trajectory labeling and policy evaluation, with published per-hour pricing.

Input Schema

NameRequiredDescriptionDefault
queryNoKeywords, e.g. 'warehouse', 'assembly', 'kitchen', 'annotation'. Empty lists everything.
domainNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description usefully adds that results include both datasets and purchasable data services with per-hour pricing, but says nothing about result volume, paging, or how keyword matching behaves.

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

Conciseness3/5

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

A single front-loaded sentence, but the tail becomes a dense services catalogue (collection, annotation incl. RGB-D/3D, trajectory labeling, policy evaluation, per-hour pricing) that reads as marketing rather than operational guidance.

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

Completeness3/5

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

With only two optional params and read-only annotations, the call mechanics are simple, but for a search tool the description gives no sense of what a result looks like (dataset entries vs service listings, whether pricing accompanies each hit) and leaves the domain dimension unexplained.

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 coverage is 50%: 'query' is documented in the schema while 'domain' is an undocumented 12-value enum. The description gestures at domain-like categories (warehouse, annotation, kitchen) but does not explain how they map to the enum or how query and domain combine.

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 states a specific verb (search) and a specific resource (VisionLibra's off-the-shelf egocentric task datasets plus associated data services), and enumerates the service categories covered. It is distinguishable from search_products by the dataset/service scope, though it never names the sibling to contrast with.

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 statement of when to pick this over search_products or get_product_specs, and no prerequisites or exclusions are given. The only usage hint ('Empty lists everything') lives in the schema, not the description.

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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