Current basis (premium/discount) snapshot for all Robinhood Chain tokenized stocks. Per ticker: chainlink_px (on-chain price), ref_last (NASDAQ), basis_bps (+ = token trades at a premium), overnight_bps (move vs last close) and the oracle_paused flag.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds useful field-level semantics, such as the meaning of basis_bps sign and the oracle_paused flag, which goes beyond the minimal annotation coverage.
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 a single, dense sentence that front-loads the core purpose and then lists the output fields efficiently. Every word adds value with no filler or repetition.
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?
With no parameters and no output schema, the description fully specifies the tool's behavior by enumerating all returned fields and their meanings. It is complete for a simple snapshot 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?
This tool takes zero parameters, so the schema coverage is trivially 100%. The description does not need to explain parameters, but it does provide helpful context about what data is returned, which is sufficient for a no-argument tool.
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 resource ('all Robinhood Chain tokenized stocks') and the action ('basis snapshot'), with a specific verb 'snapshot' that distinguishes it from history tools. It also lists the key fields, leaving no ambiguity about what is returned.
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 phrase 'Current ... snapshot' clearly indicates this is for present-time data, implicitly contrasting with hood_basis_history for historical views. It does not explicitly name alternatives but provides enough context to infer when to use it.
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.
