How to Use the Clio MCP in LlamaIndex

Q: How can I use LlamaIndex to search my Clio contacts?

First, use the listcontacts tool to fetch and index all your contacts. Then, you can build a LlamaIndex query engine over that index. This lets you ask questions like "What's the phone number for John Doe?" and get an answer derived directly from your Clio data.

Q: Can I build a RAG pipeline over my Clio documents with LlamaIndex?

Yes. You can use the listdocuments tool to get a catalog of your files and their metadata from Clio. After indexing this information, your LlamaIndex application can intelligently answer questions about which documents exist for a given matter, who created them, and when.

Q: Is it possible to filter which Clio tools my LlamaIndex agent can use?

Yes, it's straightforward. When you initialize the McpToolSpec, you can pass an allowedtools list containing the names of the only tools you want the agent to access. This is a good way to limit the agent's capabilities for security or focus.

Ground your LlamaIndex apps in live Clio data. Turn your firm's cases, contacts, and notes into a searchable knowledge base.

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Connect Clio MCP to LlamaIndex

Create your Vinkius account to connect Clio to LlamaIndex 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 Clio with LlamaIndex

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Index Your Entire Practice

Don't just call APIs; learn from them. Your LlamaIndex agent can use tools like `list_matters`, `list_contacts`, and `list_communications` to fetch data from your Clio account. The results are automatically indexed into a vector store of your choice. Now you have a private, searchable knowledge base of your firm's actual work. You can query your own data using natural language, finding connections you might have missed.

Query Your Firm in Plain English

Once your data is indexed, you can ask real questions. Try "What's the status of all pending matters in our Family Law practice?" or "Show me all notes related to the Johnson v. Smith case." Your RAG application finds the relevant documents in its index and can even call `get_matter` or `list_notes` again to get live data before giving you an answer. This process grounds the agent's response in facts straight from your Clio account. You get reliable answers backed by real-time data, not hallucinations.

Build a Case-Aware Query Engine

The `McpToolSpec` gives your LlamaIndex agent direct access to all 23 Clio tools. This allows you to build a smart query engine that decides whether to search its indexed knowledge or call a live tool. Simple lookups like `get_contact` can hit the API directly for speed. For more complex questions about case history or strategy, the agent can use its RAG pipeline to get richer context. This MCP Server setup lets you combine the power of indexed knowledge with the accuracy of live API calls.

Setup guide

Set up Clio MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Clio MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Clio tools.",
)
response = await agent.run("List recent Clio data")

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Common questions about Clio MCP in LlamaIndex

First, use the `list_contacts` tool to fetch and index all your contacts. Then, you can build a LlamaIndex query engine over that index. This lets you ask questions like "What's the phone number for John Doe?" and get an answer derived directly from your Clio data.

Yes. You can use the `list_documents` tool to get a catalog of your files and their metadata from Clio. After indexing this information, your LlamaIndex application can intelligently answer questions about which documents exist for a given matter, who created them, and when.

LlamaIndex is built for knowledge augmentation. While other frameworks just execute tool calls, LlamaIndex is designed to index the *output* of those calls. This creates a long-term memory for your agent, allowing it to learn from past interactions with your Clio data.

Yes, it's straightforward. When you initialize the `McpToolSpec`, you can pass an `allowed_tools` list containing the names of the only tools you want the agent to access. This is a good way to limit the agent's capabilities for security or focus.

All access to your Clio data, including sensitive matter details, communications, and documents, is brokered by Vinkius. The zero-trust architecture ensures that every request is authenticated. Tool execution happens in isolated environments that are destroyed after use, so your data is never stored at rest.

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