Productive MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Productive through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
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({
"productive": {
"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 Productive, 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 Productive MCP Server
Connect your Productive account to any AI agent and bring your agency management data directly into your conversation workflow.
LangChain's ecosystem of 500+ components combines seamlessly with Productive through native MCP adapters. Connect 12 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
- Projects & Budgets — List all active projects, retrieve detailed project data, and dive deep into financial budgets to monitor burn rates
- Time Tracking & Tasks — Audit logged time entries across your team and track task progress on any board instantly
- Sales & CRM — List all open deals, review the sales pipeline, and access full company/client databases without switching tabs
- Financials — Access all generated invoices and their payment statuses to keep cash flow in check
- People & Activity — Track recent activities, team availability, and audit logs to see exactly what's moving in your agency
The Productive 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 Productive to LangChain via MCP
Follow these steps to integrate the Productive 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 Productive via MCP
Why Use LangChain with the Productive MCP Server
LangChain provides unique advantages when paired with Productive through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Productive 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 Productive queries for multi-turn workflows
Productive + LangChain Use Cases
Practical scenarios where LangChain combined with the Productive MCP Server delivers measurable value.
RAG with live data: combine Productive tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Productive, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Productive tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Productive tool call, measure latency, and optimize your agent's performance
Productive MCP Tools for LangChain (12)
These 12 tools become available when you connect Productive to LangChain via MCP:
get_project
Retrieves details for a single project by ID
list_activities
Lists recent activities and audit logs
list_boards
Lists all task boards
list_budgets
Lists all project budgets
list_companies
Lists all companies (clients and partners) in the CRM
list_deals
Lists all sales deals and their current stages
list_invoices
Lists all generated invoices and their payment status
list_people
Lists all people, including employees and external contacts
list_projects
Ideal for scoping agency workload. Lists all active and archived projects in Productive
list_services
Use this to check billable items. Lists all services defined in the organization
list_tasks
Lists all tasks across the organization
list_time_entries
Lists time entries logged by the team
Example Prompts for Productive in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Productive immediately.
"Analyze our active budgets and find any approaching their limit."
"Show me unpaid invoices from last month."
"What did the development team log time on today?"
Troubleshooting Productive MCP Server with LangChain
Common issues when connecting Productive to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersProductive + LangChain FAQ
Common questions about integrating Productive 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 Productive 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 Productive to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.