Jinshuju / 金数据 MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Jinshuju / 金数据 as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Jinshuju / 金数据. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Jinshuju / 金数据?"
)
print(response)
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.
LlamaIndex agents combine Jinshuju / 金数据 tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Jinshuju / 金数据 MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Jinshuju / 金数据
Why Use LlamaIndex with the Jinshuju / 金数据 MCP Server
LlamaIndex provides unique advantages when paired with Jinshuju / 金数据 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Jinshuju / 金数据 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Jinshuju / 金数据 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Jinshuju / 金数据, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Jinshuju / 金数据 tools were called, what data was returned, and how it influenced the final answer
Jinshuju / 金数据 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Jinshuju / 金数据 MCP Server delivers measurable value.
Hybrid search: combine Jinshuju / 金数据 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Jinshuju / 金数据 to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Jinshuju / 金数据 for fresh data
Analytical workflows: chain Jinshuju / 金数据 queries with LlamaIndex's data connectors to build multi-source analytical reports
Jinshuju / 金数据 MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Jinshuju / 金数据 to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Jinshuju / 金数据 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJinshuju / 金数据 + LlamaIndex FAQ
Common questions about integrating Jinshuju / 金数据 MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.