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get_stock_brief

Analyzes a stock from 6 expert perspectives to generate a BUY/HOLD/SELL consensus with supporting arguments and real market data.

Instructions

Multi-agent AI stock brief: 6 personas debate whether to BUY, HOLD, or SELL.

Like a Rs500Cr fund meeting, 6 different experts analyse the same stock from their angle and reach a consensus using real Indian market data:

  • FII Desk: institutional flows, promoter holding, FII %

  • Algo Trader: RSI, MACD, VWAP, volume anomaly

  • Value Investor: P/E, ROE, debt ratio, credit rating

  • Retail Pulse: news tone, 52W position, India VIX

  • Macro Analyst: RBI rates, CPI inflation, G-Sec yields

  • Options Flow: PCR, max pain, OI skew

Args: symbol: NSE stock symbol (e.g. RELIANCE, TCS, HDFCBANK)

Returns JSON with: - consensus: {signal, strength, votes, disagreement} - debate: [{agent, verdict, argument, one_liner}] - the 6-way debate - agents_detail: full data + reasoning per agent

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description fully discloses the behavior: it runs 6 expert personas, returns consensus and debate data, and uses Indian market data. It does not mention rate limits or auth, but the read-only nature is clear.

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 description is well-structured with bullet points for agents and clear Args/Returns sections. It is somewhat lengthy but every sentence adds value. Front-loaded with the core concept.

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 output schema exists, the description adequately covers the return structure. It lacks details on error handling or edge cases, but for a simple parameter tool, it is sufficiently 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 coverage is 0%, but the description explicitly explains the single parameter 'symbol' with examples (e.g., RELIANCE, TCS, HDFCBANK), adding full meaning beyond the 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 it is a multi-agent AI stock brief with 6 personas debating BUY, HOLD, or SELL, using real Indian market data. This distinctively separates it from siblings like get_stock_debate and stock_quote.

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 use when a consensus from multiple perspectives is desired, but does not explicitly state when to use this tool versus alternatives like get_stock_debate or get_nifty_outlook. No when-not conditions or alternatives are mentioned.

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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