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quant-brain-mcp

by io.github.paarths-collab

scan_watchlist

Scan a watchlist and report which names did something actionable.

For each ticker: last close, day change, gap, distance from the
20/50/200-day averages and the 52-week high, ATR%, and volume vs its
20-day average -- plus which rules fired (near_52w_high, volume_spike,
crossed_above/below_200dma, at_20dma, gapped_over_1atr). Sorted
most-actionable first. The Sunday-evening tool: run it over your list,
then feed interesting names into build_trade_plan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNo1y
tickersYes

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, the description carries full behavioral burden. It discloses output format (metrics, rules fired, sorted by actionability) and implies a read-only scan. It does not mention data freshness, rate limits, or whether any state changes occur, but for a scan tool the behavior is well covered.

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 and front-loaded: the first sentence states the primary purpose, followed by a detailed enumeration of outputs. The list of rules and metrics is necessary and earns its place, though it is slightly dense.

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?

Without an output schema or annotations, the description provides substantial detail about return values (metrics, rules, sorting). It adds workflow context and names the natural follow-up tool. Missing explanation of the 'period' parameter and the exact threshold for 'actionable' keeps it from being fully complete.

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

Parameters2/5

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

Schema coverage is 0%, so description must compensate. It clarifies the 'tickers' parameter ('run it over your list'), but the 'period' parameter with its enum is never mentioned or explained. An agent has no way to know if changing the period affects the metrics or which period to set for a given use case.

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?

Description clearly states the tool scans a watchlist and reports actionable names, listing specific metrics and rules. It distinguishes itself from siblings by naming build_trade_plan as a downstream step, making its niche clear.

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

Usage Guidelines4/5

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

Provides explicit usage context ('The Sunday-evening tool') and workflow guidance ('feed interesting names into build_trade_plan'). However, it does not name alternative tools or state when not to use it, leaving some ambiguity.

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