Runn MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Runn through the 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({
"runn": {
"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 Runn, 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 Runn MCP Server
Integrate your conversational AI natively with Runn, the premier real-time resource planning and forecasting platform. This integration enables your assistant to pull essential project metadata, capacity bottlenecks, people configurations, team allocations, and timesheet metrics directly into your sessions.
LangChain's ecosystem of 500+ components combines seamlessly with Runn through native MCP adapters. Connect 12 tools via the 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
- Analyze Projects & Resources — Extract ongoing engagement details, milestones, and client associations by querying lists natively (
list_projects,list_clients). Request detailed readouts of individual operational scopes (get_project). - Audit Roles & Assignments — Find exactly who is assigned to what phase, mapping active allocations accurately (
list_assignments,list_phases). Consult team members' details (list_people,get_person) or review globally defined roles (list_roles). - Review Budgets & Actuals — Safely extract reported operational logs (
list_actuals) to compare planned work versus billed hours. Account for non-working days naturally via the holidays lists (list_holidays).
The Runn MCP Server exposes 12 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 Runn to LangChain via MCP
Follow these steps to integrate the Runn 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 12 tools from Runn via MCP
Why Use LangChain with the Runn MCP Server
LangChain provides unique advantages when paired with Runn through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Runn 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 Runn queries for multi-turn workflows
Runn + LangChain Use Cases
Practical scenarios where LangChain combined with the Runn MCP Server delivers measurable value.
RAG with live data: combine Runn tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Runn, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Runn tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Runn tool call, measure latency, and optimize your agent's performance
Runn MCP Tools for LangChain (12)
These 12 tools become available when you connect Runn to LangChain via MCP:
get_person
Retrieves details for a specific person
get_project
Retrieves details for a specific project
list_actuals
Lists actual hours logged (timesheet data)
list_assignments
Lists all resource assignments across projects
list_clients
Lists all clients in the organization
list_holidays
Lists public holidays and non-working days
list_milestones
Lists milestones for a specific project
list_people
Lists all people and resources in Runn
list_phases
Lists project phases for a specific project
list_projects
Lists all projects managed in Runn
list_roles
Lists all defined roles/positions
list_teams
Lists all teams in the workspace
Example Prompts for Runn in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Runn immediately.
"List all active projects mapped."
"Which team is assigned to the Alpha project next week?"
"What are the upcoming milestones for the Beta project?"
Troubleshooting Runn MCP Server with LangChain
Common issues when connecting Runn to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersRunn + LangChain FAQ
Common questions about integrating Runn 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 Runn 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 Runn to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.