MCP tool reference (legacy version)

Table of Contents

ping
getDataSourceSuggestions
- Example call
- Response
getRelevantQuestions
- Example call
- Response
getAnswer
- Example call
- Response
createLiveboard
- Example call
- Response

This reference guide lists the legacy MCP tools supported by the MCP Server in versions prior to 2026-05-01 and describes the MCP request input schema and data structure of the response.

ping🔗

Runs a basic health check to validate that the MCP Server is reachable.

const tsPing = await callMCPTool("ping", {});

getDataSourceSuggestions🔗

Suggests appropriate ThoughtSpot data models for a given natural language question.

Example call🔗

const dsSuggestions = await callMCPTool("getDataSourceSuggestions", {
  query: "show me sales by region" // user's query
});

Response🔗

Returns an object containing an array of suggestions:

{
  "suggestions": [
    {
      "header": {
        "guid": "worksheet-guid-123",
        "displayName": "Sales Analytics",
        "description": "Sales performance by region, product, and channel"
      },
      "confidence": 0.92,
      "llmReasoning": "This worksheet contains sales metrics and regional dimensions relevant to the query."
    }
  ]
}

Key fields are:

header.guid: Unique ID for the datasource. The datasourceId is used in getRelevantQuestions and getAnswer calls.
header.displayName: Name of the data source.
header.description: Optional description of the data source.
confidence: Numeric score indicating the confidence of the system about a data model being the right match for the user’s query.
llmReasoning: LLM’s reasoning for the suggestion.

getRelevantQuestions🔗

Uses ThoughtSpot’s reasoning engine to generate AI-suggested sub-queries that help generate specific answers for a given data context.

Example call🔗

const result = await callMCPTool("getRelevantQuestions", {
  query: "show me sales data", // User's natural language query
  datasourceIds: ["model-guid-123"], // Array of worksheet/datasource GUIDs
  additionalContext: "User is interested in the data for underperforming regions and products"
});

Response🔗

{
  "questions": [
    "What is the total sales revenue by region?",
    "Which products have the highest revenue?",
    "What are the top selling categories?"
  ]
}

Each returned question can then be passed individually into getAnswer.

getAnswer🔗

Executes a natural language query for a given data context and returns the resulting data and visualization metadata. Clients can use this data and frame URL to render visualizations.

Example call🔗

const result = await callMCPTool("getAnswer", {
  question: "Total sales by region", // Natural language question
  datasourceId: "model-guid-123" // Worksheet/datasource GUID
});

Response🔗

{
  "question": "Total sales by region",
  "session_identifier": "abc-123-def-456",
  "generation_number": 2,
  "data": "\"Region\",\"Total Sales\"\n\"East\",100000\n...",
  "frame_url": "https://...",
  "fields_info": "..."
}

Key fields are:

session_identifier: Unique session ID used to group answers. Required when creating a Liveboard from this answer using the createLiveboard MCP tool.
generation_number: Version number for this answer. Required for Liveboard creation.
question: The executed question; useful for display and to pass it into the createLiveboard request.
data: Data returned in encoded format. Contains column headers and all returned rows in comma-separated format, which can be parsed to render tables or charts in your application.
frame_url: Optional iframe URL for embedding the visualization in your UI.
fields_info: Descriptive metadata about the fields and chart, useful for explanations.

createLiveboard🔗

Creates a ThoughtSpot Liveboard with one or more answers from the results. This is a two-step process and includes the following calls:

Call getAnswer to generate visualizations and obtain session_identifier and generation_number.
Call createLiveboard with those values to create the Liveboard.

Example call🔗

const answerData = JSON.parse(answerResult.result.content);

const liveboardResult = await callMCPTool("createLiveboard", {
  name: "My Sales Dashboard",
  noteTile: "My Sales Dashboard was created by TS MCP Chat", // Description text for the Liveboard
  answers: [{
    question: answerData.question, // Display name for the Liveboard
    session_identifier: answerData.session_identifier,
    generation_number: answerData.generation_number
  }]
});

Required attributes are:

noteTile: Use this field for any Liveboard description or notes; a separate description field is not supported.
answers: Required array. Each item must include question, session_identifier, and generation_number from a prior getAnswer call.

Response🔗

{
  "liveboardId": "liveboard-guid-here",
  "name": "My Sales Dashboard",
  "frame_url": "https://..."
}

Key fields are:

liveboardId: GUID of the created Liveboard.
name: Name of the Liveboard.
frame_url: URL that can be embedded to display the Liveboard.