Mode (Collaborative Data Platform) MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Mode (Collaborative Data Platform) through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"mode-collaborative-data-platform": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Mode (Collaborative Data Platform), show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Mode (Collaborative Data Platform) through native MCP adapters. Connect 7 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Mode (Collaborative Data Platform) MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 7 tools from Mode (Collaborative Data Platform) via MCP
Why Use LangChain with the Mode (Collaborative Data Platform) MCP Server
LangChain provides unique advantages when paired with Mode (Collaborative Data Platform) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Mode (Collaborative Data Platform) MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Mode (Collaborative Data Platform) queries for multi-turn workflows
Mode (Collaborative Data Platform) + LangChain Use Cases
Practical scenarios where LangChain combined with the Mode (Collaborative Data Platform) MCP Server delivers measurable value.
RAG with live data: combine Mode (Collaborative Data Platform) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Mode (Collaborative Data Platform), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Mode (Collaborative Data Platform) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Mode (Collaborative Data Platform) tool call, measure latency, and optimize your agent's performance
Mode (Collaborative Data Platform) MCP Tools for LangChain (7)
These 7 tools become available when you connect Mode (Collaborative Data Platform) to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Mode (Collaborative Data Platform) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersMode (Collaborative Data Platform) + LangChain FAQ
Common questions about integrating Mode (Collaborative Data Platform) MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.