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GlynnCarson

tradingview-mcp

by GlynnCarson

bollinger_scan

Read-only

Scan any exchange for Bollinger Band squeezes by filtering on low Bollinger Band Width. Supports crypto and stocks across multiple timeframes.

Instructions

Scan for assets with low Bollinger Band Width (squeeze detection). Works with crypto and stocks.

This scans a whole EXCHANGE for squeezes (canonical name is exactly bollinger_scan; there is no "get_bollinger_band_analysis" tool). For the Bollinger read of ONE symbol, call coin_analysis instead.

Example: bollinger_scan(exchange="BINANCE", timeframe="15m", bbw_threshold=0.008)

Args: exchange: Exchange — crypto: KUCOIN, BINANCE, BYBIT, MEXC; stocks: EGX, BIST, NASDAQ, NYSE, BURSA, HKEX, SSE, SZSE, TWSE, TPEX timeframe: One of 5m, 15m, 1h, 4h, 1D, 1W, 1M. Typical squeeze thresholds: 15m→0.008, 1h→0.02, 4h→0.04, 1D→0.12 bbw_threshold: Maximum BBW value to filter (default 0.04) limit: Number of rows to return (max 100)

Returns list[dict] on success. On ANY failure returns a structured error envelope {"error": {"code": ..., "retryable": ...}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
exchangeNoKUCOIN
timeframeNo4h
bbw_thresholdNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true. Description adds value by disclosing return format (list of dicts) and structured error envelope on failure. No contradiction with annotations.

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?

Well-structured with a summary, example, and parameter list. Every sentence adds value. No wasted words.

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

Completeness5/5

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

Covers purpose, usage, parameters, return format, and error handling. For a complex tool with 4 optional parameters and no required params, description provides all needed context.

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

Parameters5/5

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

Schema description coverage is 0%, but description fully compensates with detailed arg explanations, enum values for exchange and timeframe, default values, and typical threshold examples. Adds significant meaning beyond the schema.

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?

Clearly states it scans for assets with low Bollinger Band Width (squeeze detection) across an exchange. Distinguishes itself from the sibling coin_analysis tool which is for single-symbol Bollinger reads.

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 guidance: use for whole exchange scanning, not for single symbol. Gives example and parameter details. Lacks explicit when-not-to-use statements but context is clear.

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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