How to Use the Bates Numbering Generator Engine MCP in Pydantic AI

Q: Is the Bates Numbering Generator Engine setup complex in Pydantic AI?

It is straightforward. You initialize an MCPToolset with the server URL and pass it into your agent, allowing for quick integration into your existing code.

Strictly typed Bates numbering for Pydantic AI document workflows.

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Connect Bates Numbering Generator Engine MCP to Pydantic AI

Create your Vinkius account to connect Bates Numbering Generator Engine 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 Bates Numbering Generator Engine with Pydantic AI

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Perplexity

Type-safe Bates numbering for Pydantic AI

Validation happens at runtime to prevent silent failures in your indexing logic. When the `generate_bates_numbers` tool returns a sequence, your agent checks it against your defined Pydantic models. If the response isn't exactly what your schema expects, the agent halts. This prevents corrupted data from entering your legal processing pipeline during high-stakes document review.

Model-agnostic indexing tools

You can swap between different backend models without changing your indexing logic. Whether you use local models or cloud-based ones, the tool acts as a consistent source of truth. By using `MCPToolset` with your agent, you keep the numbering logic decoupled from the inference engine. This makes your codebase easier to debug and maintain over time.

Runtime integrity for e-Discovery

Every call to the server is scrutinized by your agent's type definitions. This approach eliminates hallucinated indices, which is critical when you are dealing with thousands of documents. You avoid the common issue of unexpected API responses. Your agent knows the structure of the `generate_bates_numbers` output before it even executes the tool, ensuring total runtime safety.

Setup guide

Set up Bates Numbering Generator Engine 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": {
        "bates-numbering-generator-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Bates Numbering Generator Engine transactions")
print(result.output)

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Common questions about Bates Numbering Generator Engine MCP in Pydantic AI

The server output is validated against your Pydantic models at runtime. If the generated sequence doesn't match your expected schema, the agent catches the error immediately.

Yes. Since it is an MCP server, it is model-agnostic. You can pair it with whichever model meets your accuracy requirements for legal tasks.

It is straightforward. You initialize an `MCPToolset` with the server URL and pass it into your agent, allowing for quick integration into your existing code.

Yes, it supports both Streamable HTTP and SSE transports. This gives you the flexibility to choose the connection method that best fits your infrastructure.

The server performs calculations in memory and returns the result directly to your agent. It does not persist any document data, ensuring that your indexing process remains strictly private.

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