ThoughtSpot MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ThoughtSpot as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to ThoughtSpot. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in ThoughtSpot?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About ThoughtSpot MCP Server
Connect your ThoughtSpot instance to any AI agent and bring your analytics workflows directly into your chat. Search through your metadata, access reports, and list configurations natively.
LlamaIndex agents combine ThoughtSpot tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Metadata Search — Search for specific analytical assets across your organization and filter by type (Liveboards, Answers, Logical Tables)
- Browse Visualizations — List all available Liveboards (dashboards) and Answers (individual charts/tables) without leaving your environment
- Team Management — Retrieve lists of registered account users and user groups, along with their access levels
- Backend Topologies — List all configured data source connections (such as Snowflake and BigQuery) serving your platform
- Organization — Navigate through metadata tags used for classifying data objects and reports
The ThoughtSpot MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect ThoughtSpot to LlamaIndex via MCP
Follow these steps to integrate the ThoughtSpot MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from ThoughtSpot
Why Use LlamaIndex with the ThoughtSpot MCP Server
LlamaIndex provides unique advantages when paired with ThoughtSpot through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ThoughtSpot tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ThoughtSpot tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ThoughtSpot, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ThoughtSpot tools were called, what data was returned, and how it influenced the final answer
ThoughtSpot + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ThoughtSpot MCP Server delivers measurable value.
Hybrid search: combine ThoughtSpot real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ThoughtSpot to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ThoughtSpot for fresh data
Analytical workflows: chain ThoughtSpot queries with LlamaIndex's data connectors to build multi-source analytical reports
ThoughtSpot MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect ThoughtSpot to LlamaIndex via MCP:
list_account_users
Lists all users in the ThoughtSpot instance
list_answers
Lists all saved Answers (individual charts or tables)
list_data_connections
g., Snowflake, BigQuery) are connected. Lists configured data source connections
list_liveboards
Lists all available Liveboards (dashboards)
list_metadata_tags
Lists all tags used for classifying metadata objects
list_user_groups
Lists all user groups
search_metadata
Supported types include LIVEBOARD, ANSWER, LOGICAL_TABLE, etc. Search for metadata objects in ThoughtSpot by type
Example Prompts for ThoughtSpot in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ThoughtSpot immediately.
"Show me a list of all active database connections."
"Can you list all the user groups configured?"
Troubleshooting ThoughtSpot MCP Server with LlamaIndex
Common issues when connecting ThoughtSpot to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpThoughtSpot + LlamaIndex FAQ
Common questions about integrating ThoughtSpot MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ThoughtSpot with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ThoughtSpot to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.