request_dataset_materializationQueue a discovered official dataset for prioritized detached ingestion. This free idempotent operation returns a durable job ID; poll get_materialization_status until it supplies sample and query URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| discovered_dataset_id | Yes | Exact catalog ID returned by search_discovered_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds behavior beyond annotations: it is asynchronous ('queue'), returns a durable job ID, and requires polling. It also notes the operation is 'free' and 'idempotent', matching the idempotentHint annotation. It does not contradict annotations and provides meaningful behavioral detail about the detached ingestion process.
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 that are tightly packed: the action, the idempotent/queue nature, the return value, and the polling step. No extraneous words, action is front-loaded, and the title provides a concise summary.
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 asynchronous operation with no output schema, the description adequately explains the return (durable job ID) and the next step (poll status). It doesn't cover failure modes or timeout behavior, but given the simplicity and the sibling tool for status, it is reasonably complete.
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?
The input schema fully documents the sole parameter with a regex pattern and a description referencing search_discovered_datasets. The tool description adds no new parameter semantics beyond the schema's coverage, 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?
States a specific verb ('Queue') and resource ('a discovered official dataset') and explicitly names the follow-up tool (get_materialization_status) for polling. The title reinforces the purpose. This clearly distinguishes it from search_discovered_datasets (discovery) and query/sample imported dataset 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?
Implies usage: when you have a discovered dataset ID and want to make it query-ready. It explicitly instructs to poll get_materialization_status until sample and query URLs are available, framing the expected workflow. It doesn't list explicit exclusions but the context is sufficient for an agent to route to the right tool.
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.
Each tool maps to a distinct lifecycle stage: discovery, materialization, sampling, querying, aggregation, and payment. The main ambiguity is between search_public_datasets and search_discovered_datasets, plus some overlap between get_coverage_status and list_official_sources, but the descriptions provide enough guidance for most selections.
All tools use a consistent verb_object snake_case pattern with clear verbs: get_, list_, query_, request_, sample_, search_, and aggregate_. State-changing actions uniformly use request_, and status reads uniformly use get_.
With 14 tools, the server is well within the ideal range and each tool earns its place across the data lifecycle: discover, materialize, sample, query, aggregate, and manage access. The count feels complete without being padded.
The set covers discovery, materialization, sampling, querying, aggregation, coverage status, and paid access, with provenance embedded throughout. Minor gaps exist around the 'insights' promised in the server name and lifecycle operations like cancellation or removal, but agents can generally complete core workflows.