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yashv6655

Structured-Products-MCP-Server

by yashv6655

build_portfolio

Build and optimize multi-asset portfolios using modern portfolio theory with real market data, tailoring asset allocation to risk tolerance and target returns.

Instructions

Build and optimize multi-asset portfolios using modern portfolio theory with real market data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolsYesArray of stock symbols to include in portfolio (e.g., ['AAPL', 'MSFT', 'GOOGL'])
time_horizonNoAnalysis time horizon in trading days (default: 252 = 1 year)
target_returnNoTarget annual return (e.g., 0.12 for 12%)
risk_free_rateNoRisk-free rate for Sharpe ratio calculation (if not provided, fetches Treasury rate)
risk_toleranceNoRisk tolerance on 0-1 scale (0=very conservative, 1=very aggressive)
use_market_dataNoUse real-time market data for optimization
optimization_methodNoPortfolio optimization methodmax_sharpe
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'real market data' suggesting network access, but does not disclose specifics like whether it is read-only, what side effects occur, how data is fetched, or what the output looks like. The description is too high-level to set proper expectations.

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 a single, concise sentence that is front-loaded with the main verb ('Build and optimize') and resource ('multi-asset portfolios'). It contains no fluff, but could arguably include a bit more context without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, no output schema, no annotations), the description is insufficient. It does not explain what the tool returns (e.g., weights, efficient frontier), how parameters like risk_tolerance or target_return interact, or how this tool relates to the broader suite beyond a one-line summary. Users are left without critical context for a complex financial tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage with descriptive parameter details, so the baseline is 3. The description itself adds no additional parameter semantics beyond what the schema already provides, thus it does not exceed the baseline.

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?

The description clearly states the tool builds and optimizes multi-asset portfolios using modern portfolio theory with real market data. This verb+resource combination is specific and distinguishes it from related tools like optimize_black_litterman or optimize_risk_parity, though it does not explicitly name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance is provided. The reference to modern portfolio theory implies a use case, but the description does not tell the user when to prefer this over sibling tools like optimize_black_litterman or optimize_risk_parity, nor does it mention any prerequisites or exclusion scenarios.

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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