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TradingView MCP

TradingView MCP

by madiver
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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indicator_set_inputs

Updates indicator or study input values such as length, source, and period by applying JSON overrides to a chart entity, enabling quick parameter changes for analysis and debugging.

Instructions

Change indicator/study input values (e.g., length, source, period)

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": {
      "entity_id": "<entity_id>",
      "inputs": "<inputs>"
    },
    "name": "indicator_set_inputs"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputsYesJSON string of input overrides, e.g. '{"length": 50, "source": "close"}'. Keys are input IDs, values are the new values.
entity_idYesEntity ID of the study (from chart_get_state)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It states the basic mutation ('Change ... input values') but does not explain how overrides are applied, whether they replace or merge with existing values, what happens on invalid input IDs, or what the response/return value is.

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 a single compact sentence that clearly fronts the core action and includes relevant examples. There is no filler or redundancy; every part contributes to understanding the tool's purpose.

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?

The tool is relatively simple and the schema already documents the required parameters, but with no annotations or output schema, the description does not fully cover behavioral results or error conditions. An agent could invoke it correctly, but might not understand side effects or how to interpret the result.

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%, and the schema already explains both parameters, including that 'inputs' is a JSON string of overrides. The description's examples add minor illustrative value but do not provide additional semantic meaning beyond the schema.

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 uses a specific verb ('Change') and a clear resource ('indicator/study input values'), with illustrative examples ('length, source, period'). It distinguishes the tool from siblings like indicator_add or indicator_toggle_visibility, though it does not explicitly name them.

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 implies it should be used when modifying existing indicator/study input values, but it gives no explicit guidance on when not to use it or which alternative tools to prefer. There is no mention of prerequisites such as a loaded study or where to obtain the entity_id, which the schema partially covers.

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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