How to Use the Bloomberg Law MCP in Pydantic AI

Use Bloomberg Law with Pydantic AI for type-safe legal research that fails loud if data doesn't match your schema.

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Connect Bloomberg Law MCP to Pydantic AI

Create your Vinkius account to connect Bloomberg Law 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 Bloomberg Law with Pydantic AI

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Perplexity

Type-Safe Case Discovery

Run `search_legal_cases` and have the results validated against your Pydantic models immediately. If the API response misses a field, your agent stops before it does something wrong. This ensures your precedent analysis uses only clean, predictable data. It removes the risk of hallucinated fields or unexpected types in your pipeline.

Detailed Docket Access

Get granular case info by calling `get_case_details` within your Pydantic AI agent. The server returns the full record which your models validate on the fly. It handles the heavy lifting of parsing complex legal records. You get a reliable object you can trust for your legal logic.

Expert Witness Filtering

Locate expert witnesses using `search_expert_witnesses` and enforce your schema requirements on the output. It turns a broad search into a validated list of candidates. You define the structure, and the agent populates it from the Bloomberg Law data. It makes building your legal team's database much faster.

Setup guide

Set up Bloomberg Law 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": {
        "bloomberg-law-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Bloomberg Law MCP in Pydantic AI

The server returns raw data that Pydantic AI validates against your models at runtime. If the data doesn't fit, the agent raises an error immediately.

Yes. The combination of type-safe responses and the MCP Server's data lets you build logic that rejects malformed or incomplete records.

It is. Since the tools provide structured output, they work perfectly with Pydantic models. You define the requirements and the tools fill the gaps.

Vinkius provides a secure, token-authenticated endpoint. You simply configure the client and the server handles the rest of the security handshake.

We use a zero-trust architecture. Your research queries and results are never persisted on our servers, ensuring your case data stays private.

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