How to Use the Simplicate MCP in Pydantic AI

Q: Can I manage invoicing history using Simplicate and Pydantic AI?

You can list all invoices via listinvoices. Because this is financial data, Pydantic validation means you know exactly what fields (like invoice numbers or dates) are present before your agent processes them.

Q: What data does Simplicate and Pydantic AI manage regarding individual contacts?

The server uses tools like listcrmpersons. When working with this personal information, Pydantic enforces strict typing, meaning the agent can't accidentally use a string where an integer ID is expected.

Q: Is Simplicate reliable for tracking sales opportunities with Pydantic AI?

Yes. The tools listsalesopportunities and getsalesdetails ensure that every stage of the sales pipeline is captured in a type-safe, predictable manner.

Q: Does Simplicate MCP Server handle employee records with Pydantic AI?

Yes. You can use listemployees to retrieve personnel data. Pydantic confirms that the returned structure for each employee record is exactly what your application needs.

Simplicate MCP Server for Pydantic AI: Type-safe data validation for agent reliability.

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Connect Simplicate MCP to Pydantic AI

Create your Vinkius account to connect Simplicate to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Simplicate with Pydantic AI

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Capture Billable Time Using Simplicate MCP Server

`log_time_registration` allows your agent to record new hours worked, which is vital for accurate billing. Because this tool generates financial records, the data payload must be correct and validated by Pydantic. After logging time, running `list_time_registrations` confirms that the entry was correctly structured and stored.

Get Full Company Details for Agent Use

`list_crm_organizations` pulls all connected companies. Your agent can then run `get_my_organization_profile` to confirm its own identity, ensuring the context is always right. Pydantic ensures that the structured response from these calls—whether it's an ID or a name—matches what your Python code expects.

Review Project Scope and Status

`list_projects` gives you an overview of every project. If you need granular data, `get_project_details` provides everything. Crucially, `list_project_services` defines the scope of work by listing available services. This structured approach guarantees that your agent is only working with validated project boundaries.

Setup guide

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

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

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

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Common questions about Simplicate MCP in Pydantic AI

The server's output is automatically validated against your defined Pydantic models. If `list_crm_organizations` returns a field that doesn't match, the agent fails with an explicit error—no guesswork.

You can list all invoices via `list_invoices`. Because this is financial data, Pydantic validation means you know exactly what fields (like invoice numbers or dates) are present before your agent processes them.

The server uses tools like `list_crm_persons`. When working with this personal information, Pydantic enforces strict typing, meaning the agent can't accidentally use a string where an integer ID is expected.

Yes. The tools `list_sales_opportunities` and `get_sales_details` ensure that every stage of the sales pipeline is captured in a type-safe, predictable manner.

Yes. You can use `list_employees` to retrieve personnel data. Pydantic confirms that the returned structure for each employee record is exactly what your application needs.

Use it with your favorite AI tools

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

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