request_capabilityUse this state-changing tool only when existing discovery and coverage tools cannot satisfy the task. Records demand for a missing dataset, aggregation, insight, filter, freshness level, or export; repeated requests increase autonomous build priority.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | ||
| capability | Yes | ||
| source_url | No | ||
| example_query | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal state-changing and non-idempotent behavior, and the description adds the non-obvious side effect that repeated requests increase autonomous build priority. It does not discuss reversibility or return behavior, but the added context is meaningful beyond the structured hints.
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 usage gate is front-loaded and the side-effect statement follows immediately. Every sentence contributes essential guidance.
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?
The description is adequate for a simple request-recording tool: it covers purpose, usage gate, and side effects, and annotations cover mutation safety. However, without an output schema or parameter-level details, an agent is left to infer response format and the role of optional parameters.
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?
With 0% schema description coverage and four parameters, the description needed to explain the parameters. It clarifies the capability enum by listing missing capability types and mentions dataset, but it never addresses source_url or example_query, leaving a meaningful gap for optional parameters.
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 uses a specific verb ('Records demand') and a concrete resource class: missing datasets, aggregations, insights, filters, freshness levels, or exports. It also labels the tool as state-changing and contrasts it with discovery/coverage tools, making it easy to distinguish from the read-oriented siblings.
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 opening sentence provides an explicit usage gate: use only when existing discovery and coverage tools cannot satisfy the task. It does not name specific sibling tools (e.g., search_discovered_datasets or get_coverage_status), so the guidance is clear but not maximally actionable.
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.