How to Use the Xiaoshouyi / Neocrm MCP in Pydantic AI

Q: How do I connect Xiaoshouyi / Neocrm with Pydantic AI?

You pass the MCP Server endpoint to the MCPToolset. The framework handles calling the tools and then validates every piece of returned data against your defined Python models.

Q: Does Xiaoshouyi / Neocrm support getting lead details with Pydantic AI?

Yes, you call getlead. The output will be a fully validated object, guaranteeing that the data structure is always correct for your application.

Q: Can I list users from Xiaoshouyi / Neocrm using Pydantic AI?

You can call listusers. Because of Pydantic validation, you're guaranteed to get a structured list that your application code expects.

Q: How can I manage accounts with Xiaoshouyi / Neocrm using Pydantic AI?

You use listaccounts. The resulting list is immediately typed and validated against your defined schema, making the data safe for consumption.

Build type-safe agents for Xiaoshouyi / Neocrm using Pydantic AI.

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Connect Xiaoshouyi / Neocrm MCP to Pydantic AI

Create your Vinkius account to connect Xiaoshouyi / Neocrm to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Xiaoshouyi / Neocrm with Pydantic AI

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Validate all CRM data from MCP Server

The agent calls `list_contacts` and the response is automatically validated against a Pydantic model. You get predictable, clean Python objects every single time. This means you never have to worry about silent failures or unexpected field names coming back from the CRM.

Safely create new records

When using `create_lead`, your input parameters must match a schema. If the API sends back data that doesn't fit, the agent fails loudly with a validation error. This strictness prevents bad data from corrupting your system.

Structured listing of opportunities

If you list sales chances using `list_opportunities`, the results are typed and validated. You know exactly what fields—like deal value or close date—you're getting back. This correctness is critical when building mission-critical, production-grade agents.

Setup guide

Set up Xiaoshouyi / Neocrm MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "xiaoshouyi-neocrm-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Xiaoshouyi / Neocrm tools.",
)

result = await agent.run("List recent Xiaoshouyi / Neocrm transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Xiaoshouyi / Neocrm. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Xiaoshouyi / Neocrm MCP in Pydantic AI

You pass the MCP Server endpoint to the `MCPToolset`. The framework handles calling the tools and then validates every piece of returned data against your defined Python models.

Yes, you call `get_lead`. The output will be a fully validated object, guaranteeing that the data structure is always correct for your application.

You can call `list_users`. Because of Pydantic validation, you're guaranteed to get a structured list that your application code expects.

This server touches lead data, contact records, and account information. The use of Pydantic ensures that even if the raw API response is messy, your agent only processes clean, predictable fields.

You use `list_accounts`. The resulting list is immediately typed and validated against your defined schema, making the data safe for consumption.

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