One-call brief for a single Hood ticker: live quote (price, basis, pool, risk flags), research card (rating/score/thesis/catalysts), earnings risk, sector rotation context, best Reddit attention signal, 24h whale-flow summary and recent tagged news. Replaces 6-7 separate tool calls when an agent researches one ticker. Missing upstream sources fail-soft: the field is null.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds meaningful behavioral context by disclosing fail-soft behavior: 'Missing upstream sources fail-soft: the field is null.' This goes beyond the annotations and helps the agent handle missing data. No contradiction 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 moderately long but earns its length by listing all components. It is front-loaded with 'One-call brief' and compresses the list into a readable comma-separated enumeration. The final sentence about fail-soft is concise. No fluff or redundant phrasing.
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 complex with many distinct data points, and the description accounts for all of them: quote, research, earnings, sector rotation, Reddit attention, whale-flow, and news. It also explains the replacement value and the fail-soft behavior. No output schema exists, so the description carries the full burden of describing returns, and it does so sufficiently.
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 0%, so the description must compensate for the single 'ticker' parameter. It clarifies that it's a single 'Hood ticker' and implies a ticker symbol, but it does not specify format, case, or valid values. Given there is only one parameter, some guidance is provided, but more could be added (e.g., 'e.g., HOOD').
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 uses a specific verb ('brief') and clearly scopes to a single Hood ticker, enumerating the exact contents (live quote, research card, earnings risk, sector rotation, Reddit attention, whale-flow, news). It explicitly distinguishes itself from siblings by stating it 'Replaces 6-7 separate tool calls,' making its purpose unmistakable.
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: use when 'an agent researches one ticker' and that it consolidates multiple otherwise separate calls. It does not explicitly state when not to use it (e.g., when a deep dive on one specific component is needed), but the context is strong enough to guide selection.
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.
