search_public_datasetsUse this free tool first when an agent needs official US federal data but does not yet know the dataset ID. Searches normalized catalog metadata and returns matching datasets with provenance; it does not query dataset rows.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Plain-language topic, agency, or dataset keywords, such as employment, schools, or air quality. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, open-world, non-destructive behavior. The description adds useful context beyond annotations: the tool is free, searches normalized catalog metadata, returns provenance, and does not access dataset rows.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences carry the full message: usage condition, scope, behavior, and limitation. Every sentence earns its place, and the most important guidance is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter search tool with rich annotations and no output schema, the description is complete enough. It tells the agent when to use it, what it searches, what it returns, and what it deliberately avoids.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already describes the query parameter with examples. The description adds no additional parameter-level meaning, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('search'), resource ('public datasets'), and scope ('official US federal data'). It clarifies it searches catalog metadata and returns datasets with provenance, and explicitly says it does not query dataset rows, which distinguishes it from row-query tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives an explicit when-to-use condition ('when the agent does not yet know the dataset ID') and a clear exclusion ('does not query dataset rows'). It does not name alternative sibling tools explicitly, so it stops short of a perfect 5.
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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Most tools map to distinct lifecycle stages—discovery, materialization, sampling, querying, and access—but several discovery tools overlap in purpose. search_public_datasets and search_discovered_datasets both return catalog matches, and list_official_sources and get_coverage_status both describe coverage. The descriptions help separate them, but an agent could still misselect without careful reading.
All tool names follow a consistent snake_case verb_noun pattern, such as search_, list_, get_, request_, sample_, and query_. The noun phrases are descriptive and parallel, making the naming predictable across the entire set.
Fourteen tools is well within the ideal range for a public-data platform and covers discovery, materialization, sampling, querying, aggregation, coverage monitoring, capability requests, and paid access. Each tool has a justified role in the workflow, with no obvious bloat.
The tool surface covers the full data lifecycle: discover sources, search datasets, request materialization, poll status, sample, query, aggregate, and request missing capabilities. It also includes billing access and Federal Register-specific workflows, leaving no obvious dead ends for the stated domain.