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TradingView-MCP

get_indicator_tables

Read tables drawn by TradingView Pine indicators and return their contents as text grids with on-chart position, making table-only summaries such as multi-timeframe trend dashboards accessible for analysis.

Instructions

Read tables drawn by Pine indicators on a TradingView chart (e.g. a multi-timeframe trend dashboard in the corner) as text grids: grid[row][column] plus the table's on-chart position. This is the only way to read table-only summaries that have no plots or drawings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
study_idNoIndicator id from get_chart_context. Default: all indicators
chart_indexNoChart index in a multi-chart layout. Default: the active chart

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.10

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It discloses that the operation is a read of tables, describes the exact return shape (grid[row][column] plus on-chart position), and clarifies the table-only scope. It does not discuss potential errors or rate limits, but for a read-only tool the core behavior is transparent.

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 two tightly packed sentences. The first sentence front-loads the action, the resource, and the output format; the second adds a decisive scoping fact. There is no filler or repetition of schema 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?

Despite having no output schema and no annotations, the description covers what is returned, how it is structured, and when this tool is uniquely necessary. The optional parameters are fully documented in the schema, so the description plus schema is sufficient for an agent to call the tool correctly.

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?

Both parameters are already fully documented in the schema with descriptions and defaults, giving 100% schema description coverage, so the baseline of 3 applies. The description adds no parameter-level detail, but none is needed because the schema already explains study_id and chart_index adequately.

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 ('Read') and resource ('tables drawn by Pine indicators on a TradingView chart'), and precisely describes the output form as text grids with row/column plus on-chart position. The phrase 'only way to read table-only summaries that have no plots or drawings' distinguishes it from sibling tools like get_indicator_values and get_indicator_graphics.

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 gives clear context for when to use the tool: to read Pine-indicator tables, especially table-only summaries without plots or drawings. It adds a strong exclusivity signal ('This is the only way'), but it does not explicitly name or compare alternative sibling tools, so it stops short of a full when/when-not matrix.

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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