How to Use the Recruit CRM MCP in Pydantic AI

Q: How does Pydantic AI secure my client contact information from Recruit CRM?

All client contact details fetched via listcontacts are validated in memory and never stored. Vinkius runs the MCP server in an ephemeral sandbox, securing your recruiting data from end to end.

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Connect Recruit CRM MCP to Pydantic AI

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

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Type-Safe Job Applications with Pydantic AI

The `list_job_applications` tool queries candidates assigned to a job, verifying every field against strict Pydantic schemas at runtime. This MCP server verifies every field against your schemas before your application processes the candidate details. If the API returns a missing field or an unexpected data type, your agent fails immediately rather than processing corrupt information. This strict validation protects your downstream hiring pipelines from silent failures.

Validated Candidate Insertion

The `create_candidate` tool lets your agent write new talent profiles to your CRM with zero schema drift. Pydantic AI checks the candidate's name, email, and phone structure before the tool execution ever hits the network. This prevents malformed resumes from cluttering your database. Your agent catches validation errors locally, saving API credits and keeping your data clean.

Strict Company Lookups

The `get_company_details` tool fetches client records while `list_companies` retrieves your business accounts. The Pydantic AI agent parses these payloads into strongly-typed Python objects. Your code can safely access attributes like company names and address fields without worrying about runtime attribute errors. This makes your recruiting automation incredibly stable.

Setup guide

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

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

result = await agent.run("List recent Recruit CRM 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 Recruit CRM. 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.

Why Choose Vinkius

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Common questions about Recruit CRM MCP in Pydantic AI

Install the pydantic-ai-slim package with the mcp extra. Use the MCPToolset class to connect to your Vinkius HTTP endpoint, then pass that toolset to your Agent instance.

The agent will immediately raise a validation error. This prevents your pipeline from executing on corrupt or incomplete candidate profiles.

Yes, Pydantic AI is model-agnostic. You can pair this recruitment server with OpenAI, Anthropic, or local models while maintaining strict type safety.

No, you should use the unified MCPToolset approach instead. This is the recommended way to handle external MCP servers in modern Pydantic AI applications.

All client contact details fetched via `list_contacts` are validated in memory and never stored. Vinkius runs the MCP server in an ephemeral sandbox, securing your recruiting data from end to end.

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