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HPSILab Quant Finance

by io.github.haiyunsky

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
NameRequiredDescriptionDefault
forceNo
typesNo
symbolYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Discloses that force defaults to True to reflect latest data, and that it uses cached PNGs when force=False. Annotations show readOnlyHint=false and destructiveHint=false, consistent with generation. Adds context about reusing email/social generators.

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 with a clear purpose sentence, followed by context and a formatted Args section. No unnecessary words, each sentence adds value.

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 presence of an output schema, the description sufficiently covers return value (URLs). Explains all parameters and distinguishes from main sibling. Could mention relationship to get_* chart tools, but not essential.

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 Args section fully explains all three parameters: symbol with example, force with default and rationale, types with allowed values and omission behavior. This compensates completely.

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 generates stock-report PNG images and returns URLs, using a specific verb and resource. It distinguishes itself from analyze_stock by noting the separation for fast JSON analysis.

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?

Explicitly states separation from analyze_stock to keep analysis fast. Mentions backend reuse. Could be more explicit about when to use individual get_* tools instead, but provides good context.

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.

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