How to Use the Beeline MCP in Pydantic AI

Validate your workforce operations using Pydantic AI and the Beeline MCP server.

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Connect Beeline MCP to Pydantic AI

Create your Vinkius account to connect Beeline 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 Beeline with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Type-safe requisition search for Pydantic AI

Every response from `search_requisitions` is validated against your Pydantic models. If the Beeline API structure changes, your agent fails safely rather than processing bad data. Use `get_requisition` to pull full job specs into your workflow. This guarantees that your agent always operates on perfectly typed objects.

Verify timesheets with Pydantic AI

Your agent invokes `list_timesheets` to pull logs for your external team. The framework checks these records at runtime to ensure they match your expected schema. Calling `get_timesheet` lets your agent pull specific hours for audit purposes. You get a clean, validated view of your labor expenses.

Manage suppliers with strict validation

Use `list_suppliers` to pull your vendor list directly into your agent logic. Pydantic AI ensures that each supplier entry meets your strict internal formatting rules. `get_user_info` provides validated profiles for your contractor base. This gives your agent a reliable source of truth for every assignment.

Setup guide

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

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

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

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Common questions about Beeline MCP in Pydantic AI

Install the slim package and use the MCPToolset class. You point it at your Vinkius HTTP endpoint to start pulling data.

Yes, you must have the server running. The MCPToolset connects to that external instance to retrieve and validate your data.

Because the framework validates all responses, your agent will catch schema mismatches immediately. This prevents silent failures in your agent workflow.

We use a zero-trust architecture. Your credentials are never stored, and every request is signed with a short-lived token.

It handles assignment, expense, and user profile objects. Every field is strictly typed to ensure no invalid contractor data enters your system.

Use it with your favorite AI tools

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

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