How to Use the US Non-Compete Enforceability Analyzer MCP in Pydantic AI

Get type-safe legal analysis for your contracts. Use the US Non-Compete Enforceability Analyzer with Pydantic AI.

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Connect US Non-Compete Enforceability Analyzer MCP to Pydantic AI

Create your Vinkius account to connect US Non-Compete Enforceability Analyzer 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 US Non-Compete Enforceability Analyzer with Pydantic AI

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Type-safe statutory verification

Invoke `check_state_legality` to confirm if a state allows non-competes. Because you are using Pydantic AI, the response is strictly validated against your models. This prevents malformed data from reaching your agent's decision-making logic. You get a clean, typed confirmation every time.

Validated clause enforceability scoring

The `evaluate_noncompete_clause` tool returns data that your agent validates at runtime. It ensures that every clause evaluation matches your expected schema. This removes the risk of hallucinated fields or unexpected output formats. Your agent operates on verified data alone.

Enforceable threshold retrieval

Use `get_enforceability_guidelines` to pull the latest standards for contract validity. Pydantic AI ensures the returned values strictly adhere to your defined types. This consistency keeps your agent's logic sound and predictable. You can rely on the data returned to build your automated legal workflows.

Setup guide

Set up US Non-Compete Enforceability Analyzer 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": {
        "us-non-compete-enforceability-analyzer-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent US Non-Compete Enforceability Analyzer transactions")
print(result.output)

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Common questions about US Non-Compete Enforceability Analyzer MCP in Pydantic AI

You install the slim package, define the MCP toolset with your HTTP endpoint, and pass it to your Agent. Pydantic AI handles the validation of all tool responses automatically.

Yes. By validating every response against your models, Pydantic AI ensures that any unexpected data from the tools is caught immediately as a validation error.

It is. The tool outputs are structured in a way that maps perfectly to Pydantic models, allowing for full type safety across your entire agent pipeline.

This MCP is designed to process only the text of the legal clauses provided. It does not ingest or store any proprietary employee identifiers or private company contact lists.

Because legal work requires precision. Pydantic AI forces your agent to account for every piece of data, ensuring that no ambiguous or invalid information ever reaches your final report.

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