tracker_awardsQuery the 76 sourced counter-UAS awards. Filter by theatre, kill-chain layer, or awardee substring. Requires a tracker licence token.
| Name | Required | Description | Default |
|---|---|---|---|
| layer | No | Substring match, e.g. "Defeat-kinetic", "Detect", "Programme" | |
| theatre | No | Substring match, e.g. "United Kingdom" or "Eastern flank" | |
| awardee_contains | No | Substring match on the awardee, e.g. "DroneShield" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It discloses an access requirement ('Requires a tracker licence token') and the dataset scope ('76 sourced'), but it does not describe pagination, how filters combine, or the return format. 'Query' implies read-only operation, but this is implicit rather than explicit.
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 exactly two sentences: the first states the core purpose and scope, the second covers filters and the licence requirement. Every word earns its place, with no redundancy or filler.
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 tool is simple: three optional string parameters, no nested objects, no output schema. The description covers the resource, filters, and licence prerequisite, which suffices for basic invocation. However, because the output schema is absent, the agent still lacks clarity about the return shape, so a small deduction is made.
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?
Schema description coverage is 100% because all three parameters have detailed descriptions with substring examples. The tool description echoes the filter dimensions (theatre, kill-chain layer, awardee substring) but adds no new semantics beyond what the schema already provides. With high schema coverage, the baseline of 3 applies.
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 opens with a specific verb 'Query' and a concrete resource: 'the 76 sourced counter-UAS awards.' It clearly states the tool's function and distinguishes it from sibling tracker tools by identifying a unique dataset. The listing of filter dimensions adds further clarity about its scope.
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 clear context (querying a specific awards dataset) and enumerates the filter dimensions available. However, it does not explicitly contrast the tool with sibling tools like tracker_aggregate or tracker_capital_ledger, nor does it state when to prefer this tool over alternatives. Usage guidance is therefore implied rather than explicit.
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.