MCP Server with Spotter 3 capabilities

Table of Contents

Tool calls and workflow processing
Additional resources

ThoughtSpot MCP Server supports Spotter 3, which enables advanced analytics, forecasting, multi-step reasoning and analysis, and deep research capabilities. The MCP Server with Spotter 3 introduces new MCP tools, real-time streaming responses, session-based conversations, and richer interactions.

Legacy MCP Server setup MCP Server with Spotter 3

	Legacy MCP Server setup	MCP Server with Spotter 3
Functional differences	Limited capabilities for complex analysis and context integration.	Supports advanced analytics, forecasting, and deep research capabilities. Supports asynchronous streaming, session-based conversations, and context integration.
Recommended for	Maintaining existing integrations only.	All new integrations
Integration pattern Session model Response processing	Synchronous and stateless integration pattern. Each tool call is independent. Does not support persistent sessions and requires prior context to be injected manually on every follow-up call.	Asynchronous and stateful sessions. Allows multiple follow-up questions within a session. Responses are streamed with intermediate updates, including thinking narration and clarifications. Context is retained automatically across all follow-up questions within the session.
Data source selection	Requires a separate tool call (`getDataSourceSuggestions`) to retrieve source suggestions.	Built into session creation. If the `data_source_id` is not explicitly specified in the query, the most relevant source is automatically selected based on the user’s query.
Response delivery	Returns full response in a single synchronous call.	Responses are streamed incrementally; updates are accumulated via polling.
Follow-up questions	Requires prior context on every follow-up call.	The session retains context automatically. Optionally, the `additional_context` parameter can be included to inject new external context for each follow-up question or message.
API versioning support	Not available.	Supports date-based API versioning, which is identified in the MCP Server URL as `?api-version=YYYY-MM-DD`.

Functional differences

Limited capabilities for complex analysis and context integration.

Supports advanced analytics, forecasting, and deep research capabilities.
Supports asynchronous streaming, session-based conversations, and context integration.

Recommended for

Maintaining existing integrations only.

All new integrations

Integration pattern
Session model
Response processing

Synchronous and stateless integration pattern. Each tool call is independent. Does not support persistent sessions and requires prior context to be injected manually on every follow-up call.

Asynchronous and stateful sessions. Allows multiple follow-up questions within a session. Responses are streamed with intermediate updates, including thinking narration and clarifications. Context is retained automatically across all follow-up questions within the session.

Data source selection

Requires a separate tool call (getDataSourceSuggestions) to retrieve source suggestions.

Built into session creation. If the data_source_id is not explicitly specified in the query, the most relevant source is automatically selected based on the user’s query.

Response delivery

Returns full response in a single synchronous call.

Responses are streamed incrementally; updates are accumulated via polling.

Follow-up questions

Requires prior context on every follow-up call.

The session retains context automatically.
Optionally, the additional_context parameter can be included to inject new external context for each follow-up question or message.

API versioning support

Not available.

Supports date-based API versioning, which is identified in the MCP Server URL as ?api-version=YYYY-MM-DD.

The following figure illustrates the MCP architecture, tool calls, and workflow in the new MCP Server version:

Tool calls and workflow processing🔗

The workflow in the MCP Server with Spotter 3 typically includes the following stages:

User asks a question
A user sends a query in the chat interface to get data insights. For example, What were the total sales of Jackets and Bags in the Northeast last year?
Agent calls create_analysis_session
The agent calls create_analysis_session to establish a stateful analytical session with ThoughtSpot’s Spotter 3 engine. ThoughtSpot returns an analytical_session_id that identifies the session for all subsequent calls.
If the user specified a data source, the agent passes it as the optional data_source_id parameter. If not specified, ThoughtSpot automatically selects the most relevant data source based on the question.
Agent calls send_session_message
The agent calls send_session_message with the analytical_session_id and the user’s question. Optionally, the agent can pass additional_context to provide external information that should influence the analysis. For example, The user’s fiscal year starts in April or The user is a manager of the West region.
ThoughtSpot confirms receipt with a success response and begins processing the question asynchronously. The answer is not returned in this call.
Agent polls get_session_updates for results
The agent calls get_session_updates repeatedly in a loop until is_done is true. ThoughtSpot streams updates incrementally across multiple polls. The agent must accumulate updates from every poll. ThoughtSpot returns the following update types:
- text-chunk - real-time thinking narration from Spotter describing what it is doing, streamed in fragments. Concatenate chunks in order to form the complete narration.
- text - complete standalone messages representing clarifications, caveats, or assumptions Spotter made during analysis.
- answer - a final analytical result containing:
  - answer_title - a human-readable description of what the answer shows.
  - answer_query - the ThoughtSpot search query used to generate the answer.
  - iframe_url - an embeddable URL for rendering an interactive ThoughtSpot visualization.
  - answer_id - a unique identifier for the answer, used as input for creating a dashboard.
A dashboard is generated from the results (optional)
The user can choose to save answers from the conversation as a ThoughtSpot dashboard. For this workflow, the agent extracts the answer_id and answer_title from each answer type update collected during polling and calls create_dashboard.
ThoughtSpot creates a dashboard and returns a dashboard_id and a dashboard_url, a direct link to the dashboard in the ThoughtSpot UI.
User asks a follow-up question (optional)
The user can ask a follow-up question in the same session. The agent calls send_session_message again using the same analytical_session_id. ThoughtSpot retains the full conversation context automatically. The agent returns to step 4 to poll for the follow-up response.

For more information about the tool calls, input parameters, and response output, see MCP tool reference (Spotter 3).

Additional resources🔗

For information about MCP, see the Model Context Protocol specification.
For implementation details, see the MCP Server GitHub repository.