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quant-brain-mcp

by io.github.paarths-collab

generate_optimized_verdict

Optimize a portfolio and return backtest metrics plus a final verdict.

Args:
    tickers: List of ticker symbols (e.g. ["AAPL", "RELIANCE.NS"])
    amount: Investment amount used for allocation context
    optimize_type: Optimization mode. Supported values are:
        "mvo", "hrp", "max_sharpe", "min_volatility",
        "black_litterman", "cvar", "semivariance".
    period: History window for price data, e.g. "1y", "2y", "5y", "10y"
        (default "2y"). Longer windows give the optimizer and backtest
        more data at the cost of a slower fetch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountNo
periodNo2y
tickersYes
optimize_typeNomvo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that longer periods slow the fetch, indicating external data retrieval. However, it doesn't explain that the tool is read-only, what a 'final verdict' entails, or any rate limits. The behavior is partially transparent.

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 concise and well-structured: a one-sentence summary followed by an Args list. Each line adds value, with no fluff.

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?

The tool has no output schema, so the description should explain return values. It only says 'backtest metrics plus a final verdict' without specifying metrics or verdict structure. For a complex optimization tool with multiple output possibilities, this is a gap; the agent may not know what to expect.

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

Parameters4/5

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

Despite 0% schema coverage in the description, the Args section thoroughly explains each parameter: tickers with an example, amount for allocation context, optimize_type with supported values, and period with defaults and a trade-off. This adds meaning beyond the schema's enum/default definitions.

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's function: 'Optimize a portfolio and return backtest metrics plus a final verdict.' This uses specific verbs and resources, distinguishing it from sibling backtest/analysis tools that focus on single indicators or charts rather than portfolio optimization with a verdict.

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 provides parameter guidance but no explicit when-to-use vs alternatives. It implies use for portfolio optimization tasks and notes the period trade-off, but doesn't mention that for single-stock analysis one should use analyze_* or backtest_* tools.

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

C2.9/5.0
Disambiguation2/5

Several tools overlap significantly: plot_charts is an explicit alias for generate_charts, generate_chart_pack and generate_charts have similar purposes, and backtesting tools like backtest_macd_momentum vs backtest_macd_trend_follower or backtest_mean_reversion_rsi_bb vs backtest_rsi_mean_reversion are easily confused. The sector tools also have fuzzy boundaries.

Naming Consistency4/5

Most tools follow a clear verb_noun pattern (analyze_*, backtest_*, get_*, generate_*). However, two tools use a 'tool' suffix (analyze_sector_intelligence_tool, find_sector_stock_pipeline_tool) which deviates from the otherwise consistent naming style.

Tool Count3/5

At 25 tools, the server is at the heavy end of the acceptable range. The scope is broad (analysis, backtesting, charting, portfolio optimization, alerts), but redundant chart tools and overlapping backtest strategies inflate the count and hurt focus.

Completeness4/5

The toolset covers the core domain well: technical analysis, backtesting, trade planning, portfolio optimization, quotes, news, and alerts. Minor gaps exist, such as no watchlist management tool (scanning only) and no direct historical data fetch, but these are workable around the existing tools.

Resources