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quant-brain-mcp

by io.github.paarths-collab

price_alert

Persistent price alerts stored server-side (survive restarts, one-shot).

Actions:
  set    -- watch a level: price_alert("set", ticker="RELIANCE.NS",
            level=1270, direction="below", note="stop level")
  list   -- show all active alerts
  delete -- remove an alert by alert_id
  check  -- fetch current prices for every active alert and return which
            fired; fired alerts deactivate so they never spam.

A server cannot push messages into Claude, so pair this with a scheduled
task that calls action='check' on a cadence (e.g. every 30 minutes during
market hours) and notifies the user only when 'triggered' is non-empty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
levelNo
actionYes
tickerNo
alert_idNo
directionNo

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It reveals persistence, one-shot deactivation, and the inability to push messages, which are critical behaviors. It lacks details on error handling or authentication, but the most important traits are covered.

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 (around 100 words) and front-loaded with the core purpose. The action list and scheduling note are efficiently presented with no redundant filler, making every sentence valuable.

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?

Although there is no output schema, the description explains that 'check' returns fired alerts and that they deactivate. It covers the essential workflow for all actions, but could be slightly strengthened by stating what other actions return (e.g., set returns an alert_id). Overall, it is fairly complete for a tool with this complexity.

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?

Given 0% schema description coverage, this description compensates by explaining each action's parameter usage through an example and indicating role of alert_id in delete. It doesn't exhaustively document every parameter, but the provided context is sufficient for correct invocation.

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 defines the tool as managing persistent price alerts, enumerating four specific actions (set, list, delete, check) with examples. It distinguishes this from sibling analysis tools by emphasizing alert storage and retrieval.

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

Usage Guidelines5/5

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

The description explicitly states when to use the tool and how to pair it with a scheduled task for checking alerts, addressing the server push limitation. It provides a concrete usage pattern with cadence suggestions and notification guidance, which is strong usage direction.

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