Juhe Data / 聚合数据 MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Juhe Data / 聚合数据 through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Juhe Data / 聚合数据 "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Juhe Data / 聚合数据?"
)
print(result.data)
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 Juhe Data / 聚合数据 MCP Server
Empower your AI agent to access a vast array of essential data services with Juhe Data (聚合数据), the premier API aggregator in China. By connecting Juhe to your agent, you transform fragmented data retrieval into a natural conversation. Your agent can instantly check real-time weather and forecasts for any Chinese city, verify ID card registration details, lookup IP address locations, and retrieve the latest news across multiple categories. Whether you are automating background checks, monitoring local conditions, or staying updated with domestic trends, your agent acts as a real-time data intelligence assistant, providing accurate and reliable information from a single, unified source.
Pydantic AI validates every Juhe Data / 聚合数据 tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Weather Intelligence — Retrieve real-time weather and 3-day forecasts for cities across China.
- Identity Verification — Audit ID card numbers to retrieve area, sex, and birthday information.
- Geographical Insights — Lookup IP address locations to identify user regions and network providers.
- Content Aggregation — Retrieve the latest news headlines and articles across various categories.
- Calendar & Culture — Access lunar calendar data, holiday schedules, and even constellation horoscopes.
The Juhe Data / 聚合数据 MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI 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 Juhe Data / 聚合数据 to Pydantic AI via MCP
Follow these steps to integrate the Juhe Data / 聚合数据 MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 Juhe Data / 聚合数据 with type-safe schemas
Why Use Pydantic AI with the Juhe Data / 聚合数据 MCP Server
Pydantic AI provides unique advantages when paired with Juhe Data / 聚合数据 through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Juhe Data / 聚合数据 integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Juhe Data / 聚合数据 connection logic from agent behavior for testable, maintainable code
Juhe Data / 聚合数据 + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Juhe Data / 聚合数据 MCP Server delivers measurable value.
Type-safe data pipelines: query Juhe Data / 聚合数据 with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Juhe Data / 聚合数据 tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Juhe Data / 聚合数据 and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Juhe Data / 聚合数据 responses and write comprehensive agent tests
Juhe Data / 聚合数据 MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Juhe Data / 聚合数据 to Pydantic AI via MCP:
get_calendar_day
Get calendar and holiday info for a day
get_calendar_month
Get holiday info for a month
get_constellation_horoscope
Get constellation horoscope
get_driving_test_questions
Get random driving test questions
get_exchange_rate
Get currency exchange rate
get_id_card_info
Get ID card basic information
get_ip_lookup
Lookup IP address location
get_latest_news
Get latest news headlines
get_oil_price
Get latest oil prices in China
get_weather
Get weather information for a city
Example Prompts for Juhe Data / 聚合数据 in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Juhe Data / 聚合数据 immediately.
"What's the weather in Beijing today?"
"Check the information for ID card 110101199001011234."
"Show me the latest tech news from Juhe."
Troubleshooting Juhe Data / 聚合数据 MCP Server with Pydantic AI
Common issues when connecting Juhe Data / 聚合数据 to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiJuhe Data / 聚合数据 + Pydantic AI FAQ
Common questions about integrating Juhe Data / 聚合数据 MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Juhe Data / 聚合数据 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 Juhe Data / 聚合数据 to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.