How to Use the Openli MCP in Pydantic AI

Q: Does Pydantic AI support live Openli status checks?

Yes. Your agent can run checkopenlistatus on startup to verify the connection. This ensures your legal automation pipeline is fully operational before handling user traffic.

Q: Can I use SSE transports with Openli in Pydantic AI?

Yes, MCPToolset supports both SSE and Streamable HTTP transports. You can choose the transport method that best fits your external server deployment.

Run type-safe compliance checks with Pydantic AI and Openli to guarantee zero malformed privacy records.

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Connect Openli MCP to Pydantic AI

Create your Vinkius account to connect Openli 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 Openli with Pydantic AI

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Validate DSAR creation with Pydantic AI

The `create_dsar` tool creates structured privacy requests that are validated against strict Pydantic models at runtime. If the API returns unexpected fields, Pydantic AI raises a validation error immediately rather than corrupting your database. Your agent uses `get_dsar` to monitor the status of these requests. Because the schema is strictly enforced, you can trust that every field in the returned payload matches your expected Python types.

Fetch verified agreements via MCP Server

This MCP Server exposes `list_agreements` to retrieve all active legal policies hosted on your platform. Your agent uses `get_agreement` to extract the precise text and version history of a specific terms-of-service document. By wrapping the server in `MCPToolset`, Pydantic AI automatically parses the JSON responses into typed Python objects. This prevents silent failures when your agent evaluates whether an agreement needs updating.

Track user consent with runtime type safety

The `save_consent` tool lets you record visitor choices, while `get_consent` verifies existing settings. If a consent record contains an invalid timestamp or missing identifier, the validation layer catches it before it hits your database. You configure this by importing `MCPToolset` from the slim package and passing it to your agent constructor. This setup supports both HTTP and SSE transports, allowing you to stream compliance checks in real time.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Openli. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Openli MCP in Pydantic AI

Install the slim package with the mcp extra, then instantiate `MCPToolset` with your Vinkius endpoint. Pass this toolset into the `toolsets` argument of your `Agent`.

Pydantic AI validates all incoming tool data at runtime. If a tool like `list_consents` returns a malformed record, the framework raises a validation error instead of letting your agent process bad data.

Yes. Your agent can run `check_openli_status` on startup to verify the connection. This ensures your legal automation pipeline is fully operational before handling user traffic.

Yes, `MCPToolset` supports both SSE and Streamable HTTP transports. You can choose the transport method that best fits your external server deployment.

The framework communicates with the Vinkius sandbox using encrypted channels. Your consent records and audit logs are transmitted securely, preventing any exposure of user privacy choices to unauthorized external systems.

Use it with your favorite AI tools

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