get_coverage_statusUse this free operational tool to decide whether available public-data coverage is sufficient or whether to request a missing capability. Returns discovery, materialization, queryable-row, queue, failure, and freshness counts.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a read-only, idempotent, non-destructive operation, so the description doesn't need to restate these. It adds value by disclosing that the tool returns various counts (discovery, materialization, etc.), which informs the agent of the data shape. It does not mention potential rate limits or cost, but states it is 'free,' which is useful. No contradictions 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?
The description is two sentences, front-loaded with the purpose, and directly lists the return categories. Every clause earns its place without redundancy. It is concise and well-structured.
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 no-parameter, no-output-schema tool with strong annotations, this description is largely complete: it explains the raison d'être, the decision it supports, and the data it returns. It could potentially define what constitutes 'sufficient' or provide interpretation guidance for the counts, but that is not strictly required for an agent to call the tool.
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 tool has zero parameters, and the schema coverage is effectively complete since there are no properties to describe. Per the baseline rule for tools with no parameters, a score of 4 is appropriate; the description does not need to explain behavior of parameters that don't exist.
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 clearly states the tool's purpose: to decide whether public-data coverage is sufficient or if a capability should be requested. It explicitly lists the return values, making its function specific and distinguishable from generic 'status' tools. However, it does not explicitly compare itself to sibling tools like get_materialization_status, leaving some ambiguity for an agent to differentiate based on the word 'coverage'.
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 provides a clear use case: to decide whether to request a missing capability, which implies it should be used before issuing a request. This frames when to invoke it but does not explicitly list exclusions or contrast with alternatives like request_capability. A hint about when not to use it (e.g., when checking a specific dataset's materialization) would strengthen this, but the context is sufficient.
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.