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TradingView-MCP

TradingView-MCP

by tedorigawa001
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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compute_market_features

Derive non-directional market features—close-to-close returns, realized volatility, ATR, and return correlations—from aligned OHLCV bars to quantify market behavior without fetching data or recommending trades.

Instructions

Compute deterministic, non-directional features from exact-time-aligned closed OHLCV observations, typically returned by get_aligned_history: close-to-close return, realized volatility, ATR, and return correlations. It does not fetch data, fill gaps, or produce a trade recommendation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "primary_symbol": "<primary_symbol>",
      "observations": "<observations>"
    },
    "name": "compute_market_features"
  }
}

Input Schema

NameRequiredDescriptionDefault
windowNoNumber of latest observations. Default: 20
observationsYesExact-time-aligned, closed-bar observations from get_aligned_history
primary_symbolYesSymbol whose return, volatility, and ATR to compute

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.10

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations supplied, the description carries the behavioral burden and does well: it discloses that the computation is deterministic, non-directional, has no side-effect of fetching or gap-filling, and does not emit trade recommendations. It does not mention error handling, insufficient observation behavior, or return formatting, but the core side-effect profile is clearly disclosed.

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?

Two purposeful sentences: the first states what the tool computes and with what inputs; the second states three important exclusions. Every clause earns its place and the key scoping appears immediately.

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 is complete enough for an agent to select and call the tool: it explains the input provenance, the computation scope, and the main outputs. The lack of an output schema means the description could have added more about return shape, but the listed features plus full parameter schema provide sufficient operational 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?

Schema description coverage is 100%, so every parameter already has a semantic description. The tool description reinforces that observations are aligned closed bars from get_aligned_history and that primary_symbol is the object of the return/volatility/ATR computation, but it adds no new parameter-level meaning beyond the schema, which is the baseline expected at this coverage.

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 names a specific operation ('compute'), a precise resource ('deterministic, non-directional features from exact-time-aligned closed OHLCV observations'), and enumerates the concrete outputs (close-to-close return, realized volatility, ATR, return correlations). It also distances the tool from data retrieval by pointing at get_aligned_history, which disambiguates it from the many sibling analysis tools.

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?

The description clearly frames the intended context: use it on observations 'typically returned by get_aligned_history' and not to fetch data, fill gaps, or generate trade recommendations. It stops short of explicitly naming alternative tools for those adjacent tasks, so it misses the top anchor for explicit when-to-use routing, but the context is unambiguous.

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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