hpsilab-mcp

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

Name	Required	Description	Default
`symbol`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.1/5.0

Behavior4/5

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 valuable context by revealing this is a paid/pro tool with cost implications for anonymous users and a requirement to send x402 payment in the request _meta. This goes beyond the annotations and helps the agent anticipate real-world constraints.

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 well-structured with the main purpose stated first, followed by access/cost details and then an Args section. It is slightly verbose in repeating payment information, but every sentence adds relevant operational context. The front-loading of the core function aids quick comprehension.

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?

For a simple one-parameter, read-only tool with an output schema, the description covers the essential aspects: what it does, the symbol parameter, and important access/cost caveats. It lacks explicit alternative guidance, but the tool is simple enough that the provided information is largely complete.

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 input schema only specifies 'symbol' as a string with no description. The description compensates by explaining 'symbol: Stock symbol, e.g. NVDA,' providing a clear example and meaning. With schema coverage at 0%, this addition is essential and well-executed.

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 returns a 'Full pre-trade risk scan JSON for a stock,' which is a specific resource and output format. The name and title reinforce this, and it is distinct from sibling tools like get_ai_prediction or get_monte_carlo that serve different analytical purposes.

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 access and payment context (free within plan for signed-in users, per-call x402 for anonymous), but it does not explicitly state when to use this tool versus alternative risk/analysis tools. Usage is implied rather than clearly delineated, and no exclusions or alternatives are mentioned.

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

Each tool targets a distinct purpose: individual quant analyses (AI prediction, IV, Monte Carlo, option pressure, equity curve, risk scan), aggregation (analyze_stock), output generation (images, report), and account registration. No two tools are likely to be confused.

Naming Consistency5/5

All tool names follow a uniform verb_noun snake_case pattern (e.g., get_ai_prediction, generate_stock_images, register_account). The convention is applied consistently across the entire set.

Tool Count5/5

10 tools is well within the ideal 3-15 range and covers the full stock-analysis workflow: data gathering, analysis, aggregation, and report generation. Each tool contributes distinct functionality without bloat.

Completeness5/5

The tool set comprehensively covers the domain of quant stock analysis: predictive models, backtesting, options/IV analysis, risk scanning, aggregated analysis, and visual/report outputs. No obvious dead ends or missing core operations for the intended purpose.

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