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TradingView-MCP

by tedorigawa001
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record_strategy_experiment

Append a strategy experiment result to the research journal, capturing hypothesis, population, Pine versions, metrics, guardrails, and adoption decision for reproducible comparison.

Instructions

Append one exact strategy experiment result to the research journal. The record binds the hypothesis, population, Pine versions, full-ledger ids, known metrics, guardrails, and decision. It stores no OHLC or source code and never touches a chart.

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": {
      "experiment_id": "<experiment_id>",
      "hypothesis_id": "<hypothesis_id>",
      "population": "<population>",
      "methodology_version": "<methodology_version>",
      "symbol": "<symbol>",
      "timeframe": "<timeframe>",
      "baseline": "<baseline>",
      "candidate": "<candidate>",
      "conditions_matched": "<conditions_matched>",
      "minimum_trades_met": "<minimum_trades_met>",
      "decision": "<decision>"
    },
    "name": "record_strategy_experiment"
  }
}

Input Schema

NameRequiredDescriptionDefault
noteNo
symbolYes
baselineYes
decisionYes
candidateYes
timeframeYes
populationYes
experiment_idYes
hypothesis_idYes
conditions_matchedYes
minimum_trades_metYes
methodology_versionYes
parent_experiment_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.10

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose append-only behavior, data exclusions (no OHLC/source code), and no chart side effects. But it omits auth requirements, duplicate handling, and whether the operation returns a confirmation, leaving significant behavior undisclosed.

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 sentences with no wasted words. The first sentence front-loads the core purpose, and the second adds important scoping exclusions. Excellent structure for quick agent parsing.

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

Completeness3/5

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

For a 13-parameter tool with nested objects, no output schema, and no annotations, the description gives a solid overview but lacks guidance on how to map an experiment's result to fields, when to use it relative to run_strategy_experiment, and what the expected return/confirmation is.

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 0%, so the description must compensate. It groups key parameters into meaningful categories (hypothesis, population, Pine versions, full-ledger ids, metrics, guardrails, decision) which is helpful, but it leaves experiment_id, symbol, timeframe, methodology_version, and parent_experiment_id unexplained.

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 states a specific verb ('Append'), resource ('research journal'), and precise scope ('one exact strategy experiment result'). It enumerates the bound entities and explicitly excludes OHLC/source code/chart interactions, which clearly separates it from siblings that run or compare experiments.

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 journal-append context implies this is the persistence step after running a strategy experiment, and the 'never touches a chart' clause acts as an exclusion. However, no alternative tools are named and there is no explicit when-to-use or when-not-to-use guidance.

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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