How to Use the Clio MCP in LangChain

Q: How do I connect the Clio MCP Server to my LangChain agent?

You'll use the langchain-mcp-adapters package. Just instantiate the MultiServerMCPClient with your Vinkius endpoint URL, call .gettools(), and pass the resulting tool list when you create your agent. The setup is designed to be quick and get you building right away.

Q: Can a LangChain agent use Clio to log billable time?

Yes. The agent can call the createactivity tool. You just need to provide the matter it's for, the time spent, and set the type to 'TimeEntry'. This is a great way to let attorneys log hours just by talking to their agent.

Q: What's the best way to manage Clio matters using LangChain?

Start by giving your agent the listmatters and getmatter tools. This lets it see all open cases and pull details for a specific one. From there, add tools like createnote or createtask so the agent can take action on the matter it's examining.

Build legal workflow agents with LangChain. Chain Clio tools to automate case management, from client intake to final invoicing.

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Claude

Perplexity

Automate Client Onboarding

This is where chains really shine. Your agent can call `create_contact` to add a new person or company to your firm's directory. Once that's done, the agent gets the new contact ID and immediately uses it to call `create_matter`. That's it. A new client is in the system and their legal matter is officially open, all from one prompt. LangChain handles passing the output of the first tool as the input for the second. You don't have to write any glue code to connect the steps.

Assemble Case Summaries on Demand

Give your agent a matter ID and tell it to write a status update. It can use `list_notes`, `list_documents`, and `list_activities` to pull all the raw information for that specific case. This isn't just a data dump; the agent gets structured information it can understand. Then, using a ReAct-style loop, it feeds that context into a final prompt to generate a clean, human-readable summary. You can see the whole process in LangSmith, from each tool call to the final text generation, so you know exactly where the answer came from.

Your Clio MCP Server for LangChain

This isn't just about single API calls. LangChain lets you compose Clio tools into complex reasoning pipelines. An agent can `search_matters` to find a case, `list_activities` to check for recent work, and then `create_task` to schedule a follow-up with the responsible attorney. The agent decides the next best action based on the result of the previous one. This MCP server gives your agent 23 different tools to work with, turning your legal workflows into intelligent, multi-step processes.

Setup guide

Set up Clio MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Clio tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "clio-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Clio transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Clio MCP in LangChain

You'll use the `langchain-mcp-adapters` package. Just instantiate the `MultiServerMCPClient` with your Vinkius endpoint URL, call `.get_tools()`, and pass the resulting tool list when you create your agent. The setup is designed to be quick and get you building right away.

Yes. The agent can call the `create_activity` tool. You just need to provide the matter it's for, the time spent, and set the type to 'TimeEntry'. This is a great way to let attorneys log hours just by talking to their agent.

Start by giving your agent the `list_matters` and `get_matter` tools. This lets it see all open cases and pull details for a specific one. From there, add tools like `create_note` or `create_task` so the agent can take action on the matter it's examining.

It does. Since the Clio tools are standard LangChain tools, you can use them as nodes in a LangGraph state machine. This lets you build really complex, cyclical workflows, like an approval process that loops until a supervisor signs off.

Your agent's requests to access Clio data—like contact details, matter descriptions, and private notes—are processed in a dedicated V8 Isolate sandbox. Vinkius manages the connection, and each tool call runs in an ephemeral, zero-trust environment. Your data is only in memory for the duration of the call.

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