tradingview-mcp-server

by rkilchmn

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

rating_filter

Read-only

Filter cryptocurrencies by Bollinger Band rating across exchanges and timeframes to identify buy or sell signals.

Instructions

Filter coins by Bollinger Band rating.

Args: exchange: Exchange name like KUCOIN, BINANCE, BYBIT, MEXC, etc. timeframe: One of 5m, 15m, 1h, 4h, 1D, 1W, 1M rating: BB rating (-3 to +3): -3=Strong Sell, -2=Sell, -1=Weak Sell, 1=Weak Buy, 2=Buy, 3=Strong Buy limit: Number of rows to return (max 50)

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

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "rating_filter"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`rating`	No
`exchange`	No	KUCOIN
`timeframe`	No	5m

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.8.0Aug 23, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Beyond the annotations (readOnlyHint, destructiveHint), the description discloses the return type ('list[dict]') and the structured error envelope on any failure ('{"error": {"code": ..., "retryable": ...}}'). It also clarifies the limit maximum. This adds valuable behavioral context beyond the annotations.

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 with a clear one-line summary followed by a bulleted args list. It is concise and front-loads the purpose. The return/error info is appended efficiently. No extraneous content.

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?

For a filter tool with 4 optional parameters, the description covers all parameter values, return type, error envelope, and limit constraints. It is complete enough for an agent to invoke it correctly without additional information. The presence of an output schema is noted but not needed here.

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?

With schema description coverage at 0%, the description fully compensates by explaining each parameter: exchange with examples, timeframe with allowed values, rating with a mapping (-3 to +3), and limit with a max. This provides essential meaning that the schema lacks.

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 states 'Filter coins by Bollinger Band rating', which is a clear verb+resource+metric. It distinguishes the tool's core function but does not explicitly differentiate it from the sibling bollinger_scan, which likely also uses Bollinger Bands. The purpose is unambiguous and specific.

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 explains what the tool does and the parameter semantics, but it does not state when to use this tool over alternatives like bollinger_scan or other scanners. The usage context is implied from the name and description, but no explicit when/when-not guidance is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.