hpsilab-mcp

Stock Analysis

analyze_stock

Read-onlyIdempotent

Aggregate all quant tools into one JSON stock analysis.

The tool reuses the existing MCP tools as its data sources, then derives a
direction signal, direction score, bullish factors, bearish factors and
plain-English summary. If one underlying tool is gated, unavailable or
raises an error, the remaining tools still contribute to the final result
(status "partial"); if every underlying tool fails, the whole call fails
(status "error", isError=True) instead of a misleadingly "successful"
empty analysis.

Args:
    symbol: Stock symbol, e.g. "NVDA".
    refresh: Request fresh IV Radar data instead of using the backend's
        fresh IV cache. Defaults to False.
    lang: Language for `summary`, `bullish_factors` and `bearish_factors`
        - "en" (default), "zh" or "ja"; regional forms like "zh-CN" are
        accepted. Everything else in the response, `signal` included, is
        language-independent, so an existing caller that omits this gets
        byte-identical output to before.

Input Schema

TableJSON Schema

Name	Required	Default
`lang`	No	en
`symbol`	Yes
`refresh`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 7, 2026

addedInput schema / properties / lang

Added value: +{
+  "default": "en",
+  "title": "Lang",
+  "type": "string"
+}

First observedJun 24, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description richly explains failure behavior: partial results if some sub-tools fail, error if all fail. It also details that the 'lang' parameter only affects summary/factors and not the signal, ensuring callers understand output consistency. This adds significant behavioral clarity beyond the given annotations.

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 brief summary, a detailed behavioral explanation, and a clean parameter list. It is concise yet complete, with no redundant or vague statements. The flow from aggregate purpose to fallback behavior to argument details is logical and efficient.

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 description gives a complete picture of what the tool returns (direction signal, score, bullish/bearish factors, summary, and status), enough for an agent to know what to expect. It also covers edge cases (partial/error). No essential information is missing for the intended use case.

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?

Although the schema's description coverage is 0%, the description thoroughly explains each parameter in the Args section: symbol with example, refresh with its purpose and default, and lang with its effect and accepted values. It also clarifies the scope of lang's influence, making parameter usage unambiguous.

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 function as aggregating all quant tools into a single JSON stock analysis, using a specific verb ('aggregate') and resource ('quant tools'). It distinguishes itself from siblings by being a composite/aggregator, which is evident from the listing of output components (direction signal, score, factors, summary).

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 implies when to use this tool (for a comprehensive analysis) by stating it 'aggregates all quant tools' and 'reuses existing MCP tools as data sources.' It does not explicitly name alternatives (e.g., 'use get_iv_radar for just IV data'), but the aggregated nature and the phrase 'Aggregate all quant tools' make the usage context clear. Slightly more explicit guidance on when not to use it would warrant a 5.

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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