Minimal fast quote for one Hood ticker: Chainlink price (token + per-share), NASDAQ references (ref_last, ref_prev_close), basis/overnight bps, the most liquid pool's price/liquidity/deviation and oracle_paused/stale flags. Use for sizing and fill pricing instead of pulling the full 32-ticker snapshot.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive behavior. The description adds valuable context beyond annotations by stating the tool is 'minimal fast' and specifying the dynamic selection of 'the most liquid pool' plus the inclusion of oracle_paused/stale flags. No contradictions with annotations.
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 front-loaded with the core purpose in the first phrase, followed by a compact list of return fields. Two sentences only, with no filler or redundancy. Every word contributes.
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 tool has one parameter and no output schema, the description covers the essential ground: input (ticker), return fields, and use case. It lacks fine-grained error handling or formatting details, but for a simple quote tool, it is sufficiently complete for an AI agent to invoke correctly.
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, the description must compensate. It implies the 'ticker' parameter refers to a Hood ticker (from 'one Hood ticker'), which adds some meaning. However, it does not provide examples, format expectations, or valid ticker values, leaving a partial gap.
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 the tool provides a 'Minimal fast quote for one Hood ticker' and enumerates the specific data returned (Chainlink price, NASDAQ references, basis bps, pool price/liquidity/deviation, oracle flags). It distinguishes from siblings by explicitly noting it's for a single ticker and contrasts with the full 32-ticker snapshot.
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?
Explicit usage guidance is provided: 'Use for sizing and fill pricing instead of pulling the full 32-ticker snapshot.' This tells the agent when to use it and identifies the alternative (full snapshot), making the decision clear.
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.
