NCREIF 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 NCREIF through the 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 NCREIF "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in NCREIF?"
)
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 NCREIF MCP Server
Connect your NCREIF account to your AI agent and gain authoritative insights into the institutional commercial real estate market through natural conversation.
Pydantic AI validates every NCREIF tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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
- Index Performance — List and retrieve historical data for NCREIF indices such as the NPI (Property Index) and ODCE (Fund Index).
- Property Oversight — List indexed properties and fetch detailed historical returns and performance metrics.
- Market Analysis — Access high-level real estate market data and aggregated performance by region or property type (Office, Retail, etc.).
- Fund Tracking — View all tracked real estate investment funds and their performance history.
- Data Series Access — Browse granular data series and categories for in-depth real estate research.
- Deep Inspection — Fetch complete metadata for specific indices, properties, or funds using their unique IDs.
The NCREIF 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 NCREIF to Pydantic AI via MCP
Follow these steps to integrate the NCREIF 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 NCREIF with type-safe schemas
Why Use Pydantic AI with the NCREIF MCP Server
Pydantic AI provides unique advantages when paired with NCREIF 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 NCREIF integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your NCREIF connection logic from agent behavior for testable, maintainable code
NCREIF + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the NCREIF MCP Server delivers measurable value.
Type-safe data pipelines: query NCREIF with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple NCREIF tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query NCREIF and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock NCREIF responses and write comprehensive agent tests
NCREIF MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect NCREIF to Pydantic AI via MCP:
get_fund_performance
Get specific fund performance
get_index_data
Get specific index data
get_property_returns
Get property-level returns
get_property_type_data
g., Office, Retail, Industrial). Get data by property type
get_region_data
Get performance data by region
list_data_series
List available data series
list_funds
List real estate funds
list_indices
g., NPI, ODCE). List NCREIF performance indices
list_market_data
List real estate market data
list_properties
List indexed properties
Example Prompts for NCREIF in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with NCREIF immediately.
"List all commercial real estate indices available via NCREIF."
"Show me the performance data for the 'Office' property type."
"What is the recent performance history for the ODCE Fund Index?"
Troubleshooting NCREIF MCP Server with Pydantic AI
Common issues when connecting NCREIF to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiNCREIF + Pydantic AI FAQ
Common questions about integrating NCREIF 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 NCREIF 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 NCREIF to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.