hpsilab-mcp

Stock Chart Generator

generate_stock_images

Generate stock-report PNG images and return their URLs.

This is intentionally separate from analyze_stock so the JSON analysis stays
fast and light. The backend reuses the same Growth Engine image generators
used by email/social publishing.

Args:
    symbol: Stock symbol, e.g. "RXRX".
    force: Regenerate images instead of using cached PNGs. Defaults to True
        so manually requested images reflect the latest available data.
    types: Optional subset of chart types. Allowed values are
        "ai_prediction", "iv_radar", "option_pressure", "monte_carlo",
        and "equity_curves". Omit to generate every chart type.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`force`	No
`types`	No
`symbol`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior4/5

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

The description goes beyond the annotations by explaining the caching behavior ('force: Regenerate images instead of using cached PNGs'), the default value of force, and the separation from analyze_stock to keep JSON analysis fast. It does not discuss rate limits or auth, but it covers the key behavioral traits (caching, regeneration) and aligns with annotations (readOnlyHint=false, etc.). No contradiction.

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?

The description is well-structured: a one-sentence purpose, a short context note, and a clean Args list. Each sentence provides necessary information without redundancy. It front-loads the main purpose and is appropriately sized for the tool's complexity.

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?

The tool has only three parameters, one required, and an output schema is present, so the description need not explain return values. It covers all parameters, the caching behavior, and the rationale for separation from analyze_stock. Given the availability of the output schema and the tool's moderate complexity, the description is complete and provides sufficient context for an agent to select and invoke it correctly.

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?

With 0% schema description coverage, the description fully compensates by documenting each parameter: symbol with an example ('RXRX'), force with an explanation of caching and default, and types with the list of allowed values and default behavior (generate all). This significantly adds meaning beyond the raw schema.

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's purpose: 'Generate stock-report PNG images and return their URLs.' The verb 'generate' plus resource 'stock-report PNG images' is specific, and the distinction from analyze_stock ('intentionally separate... so the JSON analysis stays fast and light') helps differentiate from the sibling tool.

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?

The description explicitly contrasts with analyze_stock, indicating that this tool is for image generation while analyze_stock returns JSON analysis. It also explains the context of reuse by 'email/social publishing,' which clarifies intended usage. However, it does not mention when to use the get_* siblings versus this tool, though the image vs. data distinction is implicit.

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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