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HPSILab Quant Finance

by io.github.haiyunsky

Pre-Trade Risk Scan

get_pretrade_risk_scan
Read-onlyIdempotent

Full pre-trade risk scan JSON for a stock. Pro tool ($0.15/call via x402 for anonymous callers; free within plan limits for signed-in accounts).

Signed-in hpsilab users call this within their plan's free rate limits.
Anonymous / tokenless agents pay per call via x402 (USDC on Base) when
payments are enabled — send the x402 payment in the request _meta.

Args:
    symbol: Stock symbol, e.g. "NVDA".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, indicating safe behavior. The description adds value by disclosing pricing ($0.15/call for anonymous, free within plan for signed-in) and the x402 payment mechanism, which are behavioral traits beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is mostly concise, with a clear first sentence stating the core purpose. The payment details add length but are relevant. The structure separates general description from parameter documentation. Minor redundancy could be trimmed, but overall it's efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 required parameter, no enums, no nested objects) and the presence of an output schema, the description adequately covers input and usage context. It explains the output's nature (JSON) and pricing boundaries, though it could briefly mention what "risk scan" entails if the output schema is unavailable to the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema coverage is 0%, so the description carries the full burden. It defines the parameter "symbol" as a stock symbol with the example "NVDA," adding meaningful semantics beyond the schema's type and title. For a single required parameter, this is clear and helpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a "Full pre-trade risk scan JSON for a stock." The verb "get" is implicit, and the resource "pre-trade risk scan" is specific. It effectively distinguishes from siblings like analyze_stock or get_ai_prediction by focusing on risk scan output.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains payment models for different user types (signed-in vs anonymous) and mentions rate limits. However, it does not provide explicit guidance on when to choose this tool over sibling tools, nor does it state when not to use it.

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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TDQS

A4.4/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose, from individual analytics like AI prediction and IV radar to aggregation and report generation. No overlapping functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case, with verbs like get_, generate_, and analyze_. No mixing of conventions.

Tool Count5/5

9 tools is well-scoped for a quant finance server, covering diverse analytics and outputs without being excessive or sparse.

Completeness4/5

Covers major quant finance analyses and reporting. Minor gap: no raw data access tools, but the surface is comprehensive for the intended use case.

Resources