Trust status of the Otto data layer: per-feed freshness (age_sec/stale), 24h tick coverage, NASDAQ reference availability and oracle-pause count. Call before making trading decisions — status: ok|degraded|down.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds behavioral context by describing what status values are returned (ok|degraded|down) and the specific freshness metrics, giving the agent a clear expectation of the tool's output nature without any contradiction.
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, starts with the core purpose, and packs both the what (metrics) and the when (before trading decisions) without unnecessary filler. Every clause adds value.
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?
Given the strong annotations and the absence of an output schema, the description provides a solid picture of what the tool returns (freshness, coverage, reference availability, oracle count) and the status scale. This is fully sufficient for an agent to decide when to call and interpret the result.
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 100%, so there is no parameter documentation burden. The description adds no parameter specifics because none exist, which is appropriate and earns the baseline for no-parameter tools.
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 it reports 'Trust status of the Otto data layer' and enumerates specific data points (per-feed freshness, 24h tick coverage, NASDAQ reference availability, oracle-pause count). This differentiates it from other status-like siblings such as chain_stats or report_agent_status by specifying the exact domain and contents.
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 instruction 'Call before making trading decisions' provides an explicit use case context. However, it does not mention when not to use this tool or contrast with alternative status tools, so it stops short of full exclusions or alternatives guidance.
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.
Most tools have clearly distinct purposes, with detailed descriptions that separate e.g. hood_basis_snapshot from hood_basis_history and perp_spot_basis. A few clusters like flow_summary vs token_flows and multiple gap/basis tools could still cause slight ambiguity, but the descriptions are strong enough to guide correct selection.
All tool names follow a consistent snake_case pattern with domain prefixes (hood_, agent_, competition_, partner_), and verbs are used predictably (report_, execute_, get_, etc.). There is no mixing of conventions or vague generic names.
39 tools is on the high side, but the server covers a very broad domain (market data, signals, trading execution, competition, partner APIs, agent reporting). While many tools earn their place, a few could be consolidated (e.g., gap/basis variants), making it feel heavier than necessary for typical usage.
The surface covers the core lifecycle: market data, signals, paper/real/partner trading, position tracking, and performance reporting. Minor gaps exist (e.g., no explicit wallet balance or order cancellation), but the core workflows are well-covered and derived endpoints fill most needs.
