tracker_schemaMetadata for the paid Counter-UAS Tracker dataset: fields, theatres, layers, counts, data-through date, licence. Requires a tracker licence token.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It mentions the licence token requirement, but it doesn't explicitly characterize the operation as read-only or describe the response format. The metadata framing implies no side effects.
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?
One concise sentence packs the tool's purpose and the key prerequisite without redundancy.
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?
Despite having no output schema, the description lists the metadata components and the auth requirement, providing adequate expectation-setting for a simple schema retrieval tool. It could mention that the response is a schema object.
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 accepts no parameters, so the baseline is 4. The description adds no parameter-specific information, which is appropriate given the empty schema.
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 specifically names the 'paid Counter-UAS Tracker dataset' and enumerates its contents ('fields, theatres, layers, counts, data-through date, licence'), clearly distinguishing it from sibling data-query tools like tracker_aggregate and tracker_awards.
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 implies usage for understanding dataset metadata but doesn't explicitly state when to use it versus sibling tools. It provides a prerequisite ('Requires a tracker licence token') but no guidance on alternatives or conditions.
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 targets a distinct resource or operation. Content tools (get_briefing, get_company_profile, get_intelligence_page, compare_companies) are clearly separated by content type, while tracker_* tools each serve a unique data query function. There is no ambiguity between content retrieval and tracker analysis.
Content tools follow a verb_noun pattern (get_*, compare_companies, list_catalogue, search_content), but tracker_* tools use a noun-prefix style (tracker_aggregate, tracker_awards, tracker_capital_ledger). This mixed convention is internally consistent within each group but not throughout the server.
12 tools is well-scoped for a server covering both content access and a tracker dataset. Each tool has a clear purpose, and the number aligns with the guideline of 3-15 tools.
The tool surface covers all announced content types (briefing, intelligence page, company profile, comparison) with get/compare operations, plus catalogue listing and cross-type search. The tracker tools provide comprehensive querying and metadata access, with no obvious missing operations.