How to Use the Bloomberg Law MCP in AutoGen

Let specialized AutoGen agents debate legal strategies using real-time Bloomberg Law case data and court dockets.

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Connect Bloomberg Law MCP to AutoGen

Create your Vinkius account to connect Bloomberg Law to AutoGen 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 AutoGen

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Coordinate multi-agent debates over case law

Set up a researcher agent that runs `search_legal_cases` and an analyst agent that dissects the findings. They debate the strength of a precedent, challenging each other's interpretations of the holding. The analyst agent can request more context by asking the researcher to call `get_case_details`. This back-and-forth ensures that your legal strategy is thoroughly vetted by multiple specialized personas before you ever write a brief.

Automate adversarial docket monitoring using this MCP Server

Monitor opposing counsel by setting up one agent to run `get_docket_entries` to watch for new filings. If a new motion is detected, a second agent uses `get_filing_document` to pull the text and analyze the arguments. A third agent can then run `search_expert_witnesses` to profile the experts cited in the motion. This multi-agent pipeline turns raw court updates into a highly detailed tactical brief, ready for your lead attorney.

Cross-reference corporate profiles with active litigation

Deploy an agent to run `search_companies` and gather business intelligence on a target company. Simultaneously, another agent searches for active lawsuits using `search_court_dockets`. The agents then compare notes. If the litigation agent finds active cases via `search_state_dockets`, it passes the docket IDs to the corporate agent to assess financial risk.

Setup guide

Set up Bloomberg Law MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Bloomberg Law tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Bloomberg Law_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Bloomberg Law data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Bloomberg Law_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Bloomberg Law data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Bloomberg Law. 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 Bloomberg Law MCP in AutoGen

Agents pass the tool results through the chat context. When the researcher agent calls `get_docket_details`, the resulting JSON is posted to the group chat. The analyst agent reads this message and decides if it needs to trigger `get_filing_document`.

Yes, you control tool access at the agent level. You can give the researcher agent access to `search_federal_dockets` while keeping your writer agent focused purely on drafting. This prevents unnecessary API calls and keeps your agents focused on their specific roles.

It uses Streamable HTTP transport. You register the tools by passing your Vinkius endpoint URL to `StreamableHttpServerParams`. The `McpToolAdapter` handles the schema conversion, making the legal tools instantly available to your `AssistantAgent`.

You can manage this through AutoGen's conversation flow or by setting rate limits on your Vinkius dashboard. If agents run too many concurrent `search_court_dockets` calls, the platform queues them to ensure your connection to the Bloomberg Law API remains stable.

No retrieved files are ever cached or stored on our infrastructure. When an agent calls `get_filing_document`, the document payload is streamed directly through our ephemeral sandbox to your local AutoGen environment. Your sensitive legal files are never saved on our servers.

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