Jestor MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Jestor 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({
"jestor": {
"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 Jestor, 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 Jestor MCP Server
Empower your AI agents with Jestor's low-code internal tools platform. This MCP server allows you to list objects (tables), retrieve and list records, manage users, and monitor workflows and dashboards directly through the Jestor API. Ideal for automating internal operations and database management.
LangChain's ecosystem of 500+ components combines seamlessly with Jestor 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 Jestor 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 Jestor to LangChain via MCP
Follow these steps to integrate the Jestor 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 Jestor via MCP
Why Use LangChain with the Jestor MCP Server
LangChain provides unique advantages when paired with Jestor through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Jestor 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 Jestor queries for multi-turn workflows
Jestor + LangChain Use Cases
Practical scenarios where LangChain combined with the Jestor MCP Server delivers measurable value.
RAG with live data: combine Jestor tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Jestor, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Jestor tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Jestor tool call, measure latency, and optimize your agent's performance
Jestor MCP Tools for LangChain (10)
These 10 tools become available when you connect Jestor to LangChain via MCP:
get_me
Use this to verify connection status and current permissions. Gets current authenticated user info
get_object
Useful for understanding field types and relationships within a specific table. Retrieves details/schema for a specific object
get_record
Essential for deep-diving into a specific entry in the database. Retrieves details for a specific record
list_apps
Useful for discovering high-level toolsets available to the user. Lists all installed internal apps
list_dashboards
Use this to identify where aggregated data visualizations are located. Lists all configured dashboards
list_objects
Returns object names and labels. Use this to discover available datasets before querying specific records. Lists all objects (tables) in your Jestor account
list_records
This is the primary tool for browsing data within a table (e.g., listing all "Tasks" or "Clients"). Lists records for a specific object
list_users
Returns names, emails, and IDs. Useful for identifying record owners or system administrators. Lists all users in the organization
list_webhooks
Use this to audit third-party integrations. Lists all configured webhooks
list_workflows
Useful for auditing system logic and event-driven actions. Lists all automated workflows
Example Prompts for Jestor in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Jestor immediately.
"List all objects in my Jestor account."
"Show me the records for the 'Invoices' object."
"Check the status of my workflows."
Troubleshooting Jestor MCP Server with LangChain
Common issues when connecting Jestor to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersJestor + LangChain FAQ
Common questions about integrating Jestor 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 Jestor 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 Jestor to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.