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.

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior5/5

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

The annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, but the description adds substantial behavioral context: partial failure semantics ('status partial' if one source fails), the all-fail case producing an error, and the refresh parameter bypassing cache. These are valuable traits not derivable from annotations or schema.

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 front-loaded with the purpose in the first sentence, followed by a behavioral paragraph, then a structured Args section. Every sentence contributes details about functionality, error handling, or parameters. It is thorough but not redundant, earning a high score.

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?

With an output schema present, the description need not restate return values. It covers aggregation behavior, error scenarios, and parameter semantics. The only minor gap is the vague phrase 'all quant tools'—exact underlying tools aren't enumerated, which could matter for understanding which data sources are included. Overall, it's nearly complete.

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 carries the full burden. It clearly explains both parameters: 'symbol: Stock symbol, e.g. NVDA' and 'refresh: Request fresh IV Radar data instead of using the backend's fresh IV cache. Defaults to False.' This is exactly what the schema lacks, providing the needed meaning.

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 opens with a specific verb+resource: 'Aggregate all quant tools into one JSON stock analysis.' It then enumerates the derived outputs (direction signal, score, bullish/bearish factors, plain-English summary), which clearly distinguishes it from the sibling point-tools (e.g., get_iv_radar, get_monte_carlo) as a composite/aggregator tool.

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 description implies usage: it's for getting a consolidated view of all quant tools. However, it never explicitly states when to use this tool vs. alternatives, nor does it mention any exclusions or prerequisites. There is no direct 'use this when...' or 'instead of...' guidance, so the context is clear but not fully articulated.

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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