Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) 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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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 Mode (Collaborative Data Platform). "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Mode (Collaborative Data Platform)?"
)
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 Mode (Collaborative Data Platform) MCP Server
Connect your Mode Analytics account to any AI agent and take full control of your enterprise business intelligence, collaborative SQL reporting, and data source management through natural conversation.
LlamaIndex agents combine Mode (Collaborative Data Platform) 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
- Report Orchestration — List all managed data reports and retrieve detailed analytical parameters, including chart configurations and query states directly from your agent
- Space Navigation — Explore organizational 'Spaces' (Personal, Shared) to retrieve the exact report tokens needed to query scoped analytical boundaries natively
- Global Analytics Search — Execute workspace-wide searches to identify specific reports and datasets matching literal metadata descriptions or keywords
- Data Source Audit — Enumerate explicit database and warehouse connector sources bound to your Mode account to understand which schemas are available for querying
- Member Tracking — List statically tracked analytical users within your workspace to verify report ownership and collaborative boundaries securely
- Metadata Inspection — Deep-dive into specific Report or Space tokens to retrieve precise configuration details and chart definitions instantly
The Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) to LlamaIndex via MCP
Follow these steps to integrate the Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform)
Why Use LlamaIndex with the Mode (Collaborative Data Platform) MCP Server
LlamaIndex provides unique advantages when paired with Mode (Collaborative Data Platform) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mode (Collaborative Data Platform) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mode (Collaborative Data Platform) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mode (Collaborative Data Platform), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mode (Collaborative Data Platform) tools were called, what data was returned, and how it influenced the final answer
Mode (Collaborative Data Platform) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mode (Collaborative Data Platform) MCP Server delivers measurable value.
Hybrid search: combine Mode (Collaborative Data Platform) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) for fresh data
Analytical workflows: chain Mode (Collaborative Data Platform) queries with LlamaIndex's data connectors to build multi-source analytical reports
Mode (Collaborative Data Platform) MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Mode (Collaborative Data Platform) to LlamaIndex via MCP:
get_report
Get specific analytical parameters mapping a single tracked Mode report token
get_space
Get parameters mapping an explicitly targeted collection Space
list_data_sources
List explicit Database/Warehouse connector sources bound to Mode
list_members
List statically tracked analytical users joined within the workspace
list_reports
List static data reports generated by the Mode workspace
list_spaces
List accessible Spaces isolating datasets across the Mode workspace
search_reports
Search all reports evaluating queries natively against Mode API
Example Prompts for Mode (Collaborative Data Platform) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mode (Collaborative Data Platform) immediately.
"List all reports in my 'Shared' space"
"Search for any reports related to 'Marketing ROI' in the workspace"
"Show me the data sources currently connected to our Mode account"
Troubleshooting Mode (Collaborative Data Platform) MCP Server with LlamaIndex
Common issues when connecting Mode (Collaborative Data Platform) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMode (Collaborative Data Platform) + LlamaIndex FAQ
Common questions about integrating Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.