How to Use the Jaicob MCP in Pydantic AI

Run type-safe candidate registration with Pydantic AI and Jaicob to eliminate dirty recruitment data and API hallucinations.

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Connect Jaicob MCP to Pydantic AI

Create your Vinkius account to connect Jaicob 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 Jaicob with Pydantic AI

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Fail-Safe Candidate Creation in Pydantic AI

Avoid database corruption by validating every single applicant input at runtime. When your agent invokes `create_candidate` through this MCP Server, the payload is parsed against strict Python models before it reaches the API. If the model attempts to pass an invalid email format or missing phone number, the framework rejects the call instantly. This keeps your recruitment database clean and consistent.

Read Job Applications with Strict Schema Validation

Fetching records using `list_applications` and `list_candidates` returns structured data that conforms to your type definitions. Your code can safely assume all expected fields exist without writing endless try-except blocks. If the upstream API schema changes, your agent raises a loud validation error rather than silently processing corrupt records. This makes it ideal for production-grade hiring workflows.

Type-Safe Extraction of Recruitment Leads

Extracting opportunities with `list_recruitment_leads` and `list_clients` ensures your sales pipeline data matches your exact specifications. The framework guarantees that every organization name and lead status is correctly typed. This schema enforcement lets you pipe your support data directly into downstream CRM systems. You get clean, validated pipelines without writing manual validation code.

Setup guide

Set up Jaicob 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": {
        "jaicob-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Jaicob MCP in Pydantic AI

Initialize MCPToolset with your Vinkius HTTP endpoint and pass it to the toolsets argument of your agent. The framework takes care of parsing the schemas.

Yes, the toolset runs natively within the framework's async loop. This allows you to handle multiple candidate lookups concurrently without blocking.

The framework immediately throws a validation error. This prevents your agent from making decisions based on malformed or hallucinated fields.

Yes, this framework is entirely model-agnostic. You can back your agent with OpenAI, Anthropic, or local models while keeping the exact same tool schemas.

All validation happens locally in your runtime before transmission. The Vinkius endpoint acts as a zero-trust proxy, meaning candidate records are never stored, decrypted, or inspected in transit.

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