HPSILab Quant Finance

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

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

Output 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 observedJul 21, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description goes well beyond them: it discloses partial-failure semantics (status "partial" when one source is gated/unavailable/errors) versus whole-call failure (status "error", isError=True), plus the cache-vs-fresh behavior driven by `refresh`. That is exactly the kind of failure-mode context an agent needs before invoking an aggregator.

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 purpose sentence is front-loaded and the partial/error paragraph earns its place. The closing clause of the `lang` doc ("so an existing caller that omits this gets byte-identical output to before") is backwards-compatibility trivia that mildly pads an otherwise tight definition.

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?

For an aggregator with three parameters, an output schema, and rich annotations, nothing material is missing: inputs are fully explained, failure modes are specified, and the returned fields are summarized (which the output schema then details). An agent can invoke this correctly from the description alone.

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?

Schema description coverage is 0%, so the description must carry the load, and it does: `symbol` gets a concrete example, `refresh` is explained as bypassing the backend IV cache, and `lang` lists accepted values (en/zh/ja, regional forms like zh-CN) plus which fields it affects. All three parameters are documented with meaning the schema lacks.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

"Aggregate all quant tools into one JSON stock analysis" is a specific verb+resource, and the following sentences name the concrete outputs (direction signal, score, bullish/bearish factors, summary). It implicitly distinguishes itself from the granular siblings by saying it reuses them as data sources, but never names them, so the differentiation is inferential rather than explicit.

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 aggregation-over-siblings intent is implied by "reuses the existing MCP tools as its data sources", which suggests using this instead of calling get_iv_radar, get_ai_prediction, etc. individually. However, there is no explicit when-to-use/when-not statement and no guidance on when a single-component sibling is preferable to the aggregate bundle.

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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