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quant-brain-mcp

by io.github.paarths-collab

build_trade_plan

Build a sized trade plan: entry, stop, share count, R targets, invalidation.

The answer to "what do I do?": given account equity and the percent of it
you are willing to risk, returns an entry reference (last close), a
structural stop (tighter of recent swing level or 2x ATR, never inside
daily noise), the exact number of shares so a stop-out loses only the
risk budget, 1R/2R/3R targets, a liquidity check (order as % of 20-day
turnover), and a one-line invalidation. Educational analysis, not
investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
equityYes
periodNo1y
tickerYes
risk_pctNo
directionNolong

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden, and it delivers a detailed account of the tool's behavior: it returns entry, structural stop logic, exact share count, R targets, liquidity check, and invalidation. It also includes the educational disclaimer. While it doesn't mention side effects like rate limits or auth, those may be irrelevant, and the algorithm transparency is commendable.

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 front-loaded: the first sentence gives a crisp summary, and the second expands with valuable specifics. Every sentence earns its place without fluff or repetition.

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?

The description thoroughly explains what the tool returns and the core logic, making it complete enough given the absence of an output schema and annotations. It could mention data sources or edge cases, but the essentials are well covered, and the disclaimer adds context.

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 schema has no parameter descriptions (0% coverage), so the description must compensate. It clearly explains 'equity' as account equity and 'risk_pct' as the percent of equity willing to be risked. However, it does not describe 'ticker', 'period', or 'direction', leaving these parameters with only their names and types to guide the agent. Partial compensation only.

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 and resource: 'Build a sized trade plan', clearly stating the tool's purpose. It enumerates the key outputs (entry, stop, share count, R targets, invalidation), distinguishing it from sibling tools that perform analysis or backtesting rather than generating actionable trade plans.

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 positions the tool as 'the answer to what do I do?', which implies usage when a user wants an actionable plan based on equity and risk. However, it does not explicitly state when to use this over alternatives or when not to use it, so the guidance is implied rather than direct.

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