How to Use the Enrich CRM MCP in Pydantic AI

Strictly validated contact data and company technographics for your Pydantic AI agents.

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Connect Enrich CRM MCP to Pydantic AI

Create your Vinkius account to connect Enrich CRM 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 Enrich CRM with Pydantic AI

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Perplexity

Type-safe company enrichment

The `enrich_company` tool returns structured funding data and tech stack details for any domain. This MCP Server feeds that data directly into your agent's workflow. Pydantic AI catches errors before they corrupt your database. If the enrichment API returns a string instead of an expected integer for employee count, the framework fails loudly rather than silently passing bad data to your CRM.

Hallucination-free contact lookup

Guessing emails burns hours you don't have. The `find_email` and `find_phone` tools hit verified external endpoints to return exact, real-world contact details. LLMs love to invent email addresses. By forcing the agent to use these specific tools, you guarantee every returned contact maps to a real Pydantic model validated at runtime.

Account status verification via MCP tool

Working blind causes duplicate outreach. The `get_account_info` tool checks your internal system to see if an account is active, while `enrich_person` pulls the target's current job title. You define the exact schema for these responses. The agent executes the MCP tool, Pydantic parses the JSON, and your application receives a perfectly typed Python object ready for production use.

Setup guide

Set up Enrich CRM 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": {
        "enrich-crm-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Enrich CRM transactions")
print(result.output)

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Common questions about Enrich CRM MCP in Pydantic AI

Use the MCPToolset class with your HTTP endpoint. Pass it directly to your Agent instance under the toolsets parameter.

No. That class is deprecated. Stick to the unified MCPToolset approach for all remote connections.

Yes. The framework is entirely model-agnostic. You can run Llama locally and still call these enrichment endpoints.

Pydantic throws a validation error immediately. The agent stops execution, preventing malformed data from reaching your system.

Tech stack arrays exist only for the duration of the request. The container destroys the payload the second Pydantic validates the response.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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Pydantic AI sdk-python

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