hpsilab-mcp

Stock Research Report

generate_stock_research_report

Full markdown research report with five stock-report charts. Pro tool ($0.35/call via x402 for anonymous callers; free within plan limits for signed-in accounts, subject to a monthly report quota).

Runs analyze_stock and stock-report image generation concurrently, then
renders a presentation-ready markdown report (direction, direction score, bullish /
bearish factors, source-tool status, and the five chart embeds). The markdown
is returned for display and the same data is mirrored in structured JSON.

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. "RXRX".
    refresh: Bypass the backend's fresh IV cache for the IV-driven modules.
        Defaults to False.
    force_images: Force a fresh image render instead of reusing the backend's
        image cache. Defaults to False.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	Yes
`refresh`	No
`force_images`	No

TDQS

A4.8/5.0

Behavior5/5

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

Discloses concurrency, payment via x402, free quota, cache behavior for IV and images, and output format (markdown + JSON). Provides context beyond annotations without contradicting them.

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

Conciseness5/5

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

Structured with a short intro followed by Args; each sentence adds distinct value (cost, behavior, output, caching). No filler or repetition.

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

Completeness5/5

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

Covers return value (markdown + JSON), payment/auth, parameter behavior, and pipeline composition. Sufficient for an agent to invoke correctly even without an output schema.

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

Parameters5/5

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

All three params are explained with an example for symbol, and refresh/force_images describe their effect (bypass IV cache, force fresh image render) and defaults. This fully compensates for zero schema coverage.

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?

Description clearly states it generates a full markdown research report with five charts, and distinguishes from siblings by noting it runs analyze_stock and image generation concurrently.

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

Usage Guidelines4/5

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

Sets context as a 'Pro tool' with pricing/quota, and explains it combines analyze_stock and image generation, implying it's the comprehensive option. However, it doesn't explicitly say when to prefer this over calling siblings separately.

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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