How to Use the Moka HR MCP in Pydantic AI

Q: How do I install the Moka HR tools in Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp" and initialize MCPToolset with your server URL. Pass this toolset directly to your Agent instance.

Q: Can Pydantic AI handle missing fields when querying Moka HR?

Yes, the SDK validates the responses from tools like getcandidate against strict schemas. If a candidate record lacks optional fields, Pydantic handles them gracefully using your defined default values.

Q: How do I list open roles using Pydantic AI?

Your agent calls listjobs to retrieve all active positions. The returned list is parsed into typed Python objects, making it easy to filter or search programmatically.

Run type-safe recruiting pipelines with Pydantic AI and the Moka HR MCP server.

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Connect Moka HR MCP to Pydantic AI

Create your Vinkius account to connect Moka HR 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 Moka HR with Pydantic AI

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Type-safe candidate creation with Pydantic AI

The `create_candidate` tool ensures that every candidate profile added to your database matches your exact data schema. If a resume parser returns malformed fields, the SDK triggers a validation error immediately. This strict validation prevents incomplete candidate records from entering your Moka HR database. Your agent can confidently process high volumes of resumes without risking database corruption or silent field mismatches.

Validate application summaries at runtime

The `get_hiring_summary` tool extracts applicant progress and recruiter notes into a structured format. Pydantic AI validates this payload against your Python models before exposing it to your downstream application logic. If the API structure changes, your pipeline fails loudly instead of passing bad data to your team. This guarantees that your automated hiring reports remain accurate and reliable.

Track interview status via MCP Server

The `list_interviews` tool retrieves scheduled interview details with strict type-safety guarantees. Your agent can safely verify dates, times, and interviewer IDs without casting raw JSON fields manually. When pulling specific feedback with `get_interview`, the SDK enforces strict type checking on the response. This ensures your scheduling agents always work with valid calendar data.

Setup guide

Set up Moka HR 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": {
        "moka-hr-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Moka HR MCP in Pydantic AI

Install the package using pip install "pydantic-ai-slim[mcp]" and initialize MCPToolset with your server URL. Pass this toolset directly to your Agent instance.

Yes, the SDK validates the responses from tools like `get_candidate` against strict schemas. If a candidate record lacks optional fields, Pydantic handles them gracefully using your defined default values.

Yes, the framework is model-agnostic and works with local LLMs or commercial APIs. The server handles tool execution identically regardless of which model drives your agent.

Your agent calls `list_jobs` to retrieve all active positions. The returned list is parsed into typed Python objects, making it easy to filter or search programmatically.

The server processes sensitive interview feedback and candidate histories through ephemeral V8 isolates. The SDK validates the data structure locally, ensuring no unencrypted personal information leaks during transmission.

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