JobProgress (Leap) MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect JobProgress (Leap) 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({
"jobprogress-leap": {
"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 JobProgress (Leap), 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 JobProgress (Leap) MCP Server
Empower your AI agents with JobProgress's (now part of Leap) business management platform for contractors. This MCP server allows you to list and retrieve customers and jobs, track estimates and proposals, manage workflows and tasks, and view appointments directly through the JobProgress API. Ideal for automating construction and home improvement operations.
LangChain's ecosystem of 500+ components combines seamlessly with JobProgress (Leap) through native MCP adapters. Connect 10 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.
The JobProgress (Leap) MCP Server exposes 10 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 JobProgress (Leap) to LangChain via MCP
Follow these steps to integrate the JobProgress (Leap) 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 10 tools from JobProgress (Leap) via MCP
Why Use LangChain with the JobProgress (Leap) MCP Server
LangChain provides unique advantages when paired with JobProgress (Leap) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine JobProgress (Leap) 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 JobProgress (Leap) queries for multi-turn workflows
JobProgress (Leap) + LangChain Use Cases
Practical scenarios where LangChain combined with the JobProgress (Leap) MCP Server delivers measurable value.
RAG with live data: combine JobProgress (Leap) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query JobProgress (Leap), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain JobProgress (Leap) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every JobProgress (Leap) tool call, measure latency, and optimize your agent's performance
JobProgress (Leap) MCP Tools for LangChain (10)
These 10 tools become available when you connect JobProgress (Leap) to LangChain via MCP:
get_customer
Returns addresses, project history, and custom metadata. Essential for deep intelligence on a customer before preparing estimates or jobs. Retrieves details for a specific customer
get_job
Returns project descriptions, cost estimates, and current stage in the workflow. Use this to analyze a project's progress or provide customer updates. Retrieves details for a specific job
list_appointments
Essential for managing the field service calendar. Lists all scheduled appointments
list_customers
Returns customer names, contact info, and IDs. Use this as the main starting point for customer management or finding a specific client. Lists all customers in JobProgress
list_estimates
Useful for auditing sales performance and identifying pending project approvals. Lists all job estimates
list_jobs
Includes job titles, status, and associated customer IDs. Use this to monitor the project pipeline and upcoming work. Lists all jobs in JobProgress
list_proposals
Useful for monitoring contract acceptance and sales conversions. Lists all job proposals
list_tasks
Essential for helping the user manage their daily to-do list. Lists all tasks
list_users
Useful for identifying assignees or sales reps. Lists all users in the organization
list_workflows
Useful for understanding business processes. Lists all configured workflows
Example Prompts for JobProgress (Leap) in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with JobProgress (Leap) immediately.
"List all active customers in JobProgress."
"Show me the details for job ID '123'."
"Check my appointments for today."
Troubleshooting JobProgress (Leap) MCP Server with LangChain
Common issues when connecting JobProgress (Leap) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersJobProgress (Leap) + LangChain FAQ
Common questions about integrating JobProgress (Leap) 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 JobProgress (Leap) 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 JobProgress (Leap) to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.