list_official_sourcesUse this free tool to inspect which federal, state, city, police, education, and other official source scopes AgentNative currently covers. Returns each source's importer, discovery state, and materialized coverage; it does not return dataset rows.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond those: it is a free tool, it returns per-source importer/discovery/materialized coverage, and it explicitly excludes dataset rows. No contradiction 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The primary purpose is front-loaded, and the second sentence adds exact return fields plus a useful exclusion. Every word earns its place.
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?
With no parameters and no output schema, the description fully specifies the return contents (importer, discovery state, materialized coverage) and the key non-return (dataset rows). This is sufficient for an agent to decide whether to call this tool and what to expect from it.
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?
This tool has zero parameters, so the baseline is 4. The description does not need to explain parameter meaning, and it correctly focuses on the output semantics instead.
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 ('inspect') and resource ('official source scopes AgentNative currently covers'), and immediately clarifies what the tool returns and what it does not return. It clearly differentiates from siblings like list_imported_datasets by emphasizing scope coverage rather than dataset rows.
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?
The description gives clear context for use: when you need to know which official source scopes are covered and their importer/discovery/materialization state. It does not explicitly name alternative tools or provide when-not-to-use guidance, but it disambiguates by stating that dataset rows are not returned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target clearly distinct actions and states, such as searching, sampling, querying, materializing, or requesting paid access. The main ambiguity is between search_public_datasets and search_discovered_datasets, which both search catalog metadata and differ mainly in scope and materialization-state reporting.
All tool names follow a consistent snake_case verb_noun pattern, using clear verbs like list, search, sample, query, request, get, and aggregate. The naming makes the action and subject predictable across the entire set.
With 14 tools, the server covers distinct stages of a coherent workflow: discovery, materialization, status polling, sampling, querying, aggregation, coverage checking, and paid access. Each tool addresses a meaningful step without excessive redundancy.
The tool surface covers the full discovery-to-paid-query lifecycle for imported government datasets and includes dedicated Federal Register access. Minor gaps exist, such as no explicit dataset-detail or payment-status tool, but agents can work around these using list/search and request_paid_access.