rcq_statusFree operational check for RCQ Data Oracle. Use before paid calls to confirm product availability and freshness. Returns a filtered operational summary only, not paid market intelligence or internal infrastructure details.
| Name | Required | Description | Default |
|---|---|---|---|
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the disclosure burden. It states the tool is free, returns only a filtered operational summary, and explicitly excludes paid market intelligence and internal infrastructure details. This gives useful boundary information beyond the tool name, though it does not detail response behavior or potential side effects, which is acceptable for a status endpoint.
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 concise sentences with no filler. The most important information (free, operational check, use-before-paid-calls) is front-loaded, and the exclusionary details follow naturally 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?
For a parameterless status check with no output schema, the description adequately explains what it returns and what it explicitly does not return. It could mention format or typical usage cadence, but the combination of purpose, trigger condition, and boundary information is sufficient for an agent to decide whether to use this 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, so the schema is trivially complete and the description does not need to explain parameter meaning. The baseline of 4 applies here, and the description adds no parameter information because none is needed.
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 identifies the tool as a free operational status check for the RCQ Data Oracle and specifically says it confirms product availability and freshness. It also distinguishes itself from paid calls by stating it is not paid market intelligence or internal infrastructure details, which helps an agent tell it apart from the broader data 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?
The description explicitly advises using this tool before paid calls to check availability and freshness, giving a clear trigger condition. It does not enumerate alternative tools or exclusions, but the purpose is specific enough that the agent can infer when this is appropriate compared to the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool addresses a clearly distinct purpose: DCA accumulation intensity, funding regime compact state, full funding surface, comprehensive BTC snapshot, macro event risk, monthly performance series, and operational status. The only potential overlap is between btc_funding_regime and btc_funding_surface, but the descriptions explicitly differentiate them as a cheap branch-on-state check versus the full surface, eliminating ambiguity.
All tool names use lowercase snake_case and follow a noun-phrase pattern with domain-prefixed names (btc_, macro_, monthly_, rcq_). They are descriptive and consistent in style, with no mixing of conventions like camelCase or varied verb usage. The naming clearly signals the data domain and specific focus of each tool.
Seven tools is a well-scoped set for a specialized BTC market intelligence oracle. Each tool earns its place by covering a distinct data need—regime checks, funding surfaces, macro context, historical series, and operational status—without redundancy or missing core functionality. The count is neither too sparse nor overwhelming.
The tool surface provides comprehensive coverage for its stated purpose: real-time and historical market intelligence, funding regime and surface analysis, DCA strategy indicator, macro risk events, and monthly performance data. The inclusion of a status tool ensures operational reliability. There are no obvious gaps that would cause agent failures, as the domain is clearly scoped to BTC-centric data.