How to Use the Fountain MCP in Pydantic AI

Q: How do I connect the Fountain MCP Server to Pydantic AI?

Run pip install "pydantic-ai-slimmcp". Initialize MCPToolset with your HTTP endpoint URL. Pass it in the toolsets array when building your Agent.

Build type-safe hiring agents with Pydantic AI and the Fountain MCP Server to eliminate silent data corruption.

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Connect Fountain MCP to Pydantic AI

Create your Vinkius account to connect Fountain 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 Fountain with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Fetch Validated Applicants

Pulling candidate records relies on the `get_applicant` and `list_applicant_notes` tools. Your agent retrieves the raw profile data and any recruiter feedback attached to the file. It gets the exact history of that candidate. Pydantic AI forces this data into strict schemas at runtime. If the API returns a string where an integer should be, the agent fails loudly. You never end up with corrupted applicant profiles polluting your downstream systems.

Pydantic AI Interview Tracking

Tracking scheduled conversations and successful hires uses the `list_interview_sessions` and `list_workers` tools. The system pulls interview times and matches them against the roster of active employees. It calculates conversion rates based on hard numbers. Model-agnostic execution means you run this logic anywhere. You might use OpenAI for complex interview analysis and a local model for basic worker counting. The Fountain MCP Server responds exactly the same way regardless of the LLM driving it.

Audit Funnel Configuration

Auditing your pipeline setup requires the `list_funnels`, `list_funnel_stages`, and `get_account_details` operations. The agent maps out your entire organizational structure and hiring workflow. It checks that your funnels align with corporate standards. Connecting this via the unified `MCPToolset` approach keeps your code clean. You point the toolset at the HTTP endpoint, and Pydantic AI handles the rest. The integration stays stable even as your hiring processes evolve.

Setup guide

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

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

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

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Common questions about Fountain MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"`. Initialize `MCPToolset` with your HTTP endpoint URL. Pass it in the `toolsets` array when building your Agent.

The framework throws a validation error immediately. It refuses to process malformed data, protecting your system from hallucinated fields or broken records.

Yes. Pydantic AI is completely model-agnostic. You can query the MCP server using Anthropic, Gemini, or a local model running on your own hardware.

Yes. The framework supports both Streamable HTTP and SSE transports. You just configure the toolset URL to match your preferred connection method.

We secure applicant notes and account details at the infrastructure level. The system uses a zero-trust architecture where auth is handled externally. No persistent data storage exists on the Vinkius platform.

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