Jinshuju / 金数据 MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Jinshuju / 金数据 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({
"jinshuju": {
"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 Jinshuju / 金数据, 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 Jinshuju / 金数据 MCP Server
Empower your AI agent to orchestrate your data collection workflows with Jinshuju (金数据), the premier online form builder in China. By connecting Jinshuju to your agent, you transform complex form management, entry auditing, and lead collection into a natural conversation. Your agent can instantly list your forms, retrieve detailed submission data, create new entries programmatically, and even monitor webhook configurations without you ever needing to navigate the comprehensive web interface. Whether you are managing customer surveys or automated registration flows, your agent acts as a real-time data coordinator, keeping your information accurate and your responses organized.
LangChain's ecosystem of 500+ components combines seamlessly with Jinshuju / 金数据 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.
What you can do
- Form Orchestration — List all forms and retrieve detailed structures, field definitions, and settings.
- Entry Management — List, view, create, and update form submissions with full field support.
- Data Auditing — Retrieve real-time entry counts and monitor submission velocity for your forms.
- Webhook Control — Browse and monitor configured webhooks to ensure your data pipelines are healthy.
- Workflow Integration — Programmatically submit or modify entries to bridge your AI workflows with form data.
The Jinshuju / 金数据 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 Jinshuju / 金数据 to LangChain via MCP
Follow these steps to integrate the Jinshuju / 金数据 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 Jinshuju / 金数据 via MCP
Why Use LangChain with the Jinshuju / 金数据 MCP Server
LangChain provides unique advantages when paired with Jinshuju / 金数据 through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Jinshuju / 金数据 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 Jinshuju / 金数据 queries for multi-turn workflows
Jinshuju / 金数据 + LangChain Use Cases
Practical scenarios where LangChain combined with the Jinshuju / 金数据 MCP Server delivers measurable value.
RAG with live data: combine Jinshuju / 金数据 tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Jinshuju / 金数据, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Jinshuju / 金数据 tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Jinshuju / 金数据 tool call, measure latency, and optimize your agent's performance
Jinshuju / 金数据 MCP Tools for LangChain (10)
These 10 tools become available when you connect Jinshuju / 金数据 to LangChain via MCP:
create_entry
Submit a new entry
delete_entry
Delete an entry
get_entry
Get entry details
get_entry_count
Get total entry count
get_form
Get form details
get_form_fields
Get form field definitions
list_entries
List form entries
list_forms
List all forms
list_webhooks
List form webhooks
update_entry
Update an entry
Example Prompts for Jinshuju / 金数据 in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Jinshuju / 金数据 immediately.
"List all my forms in Jinshuju."
"Show me the last 5 entries for form 'ABC-123'."
"Submit a new entry to form 'XYZ-789' with name 'John Doe' and email 'john@example.com'."
Troubleshooting Jinshuju / 金数据 MCP Server with LangChain
Common issues when connecting Jinshuju / 金数据 to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersJinshuju / 金数据 + LangChain FAQ
Common questions about integrating Jinshuju / 金数据 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 Jinshuju / 金数据 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 Jinshuju / 金数据 to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.