How to Use the Actionstep MCP in LlamaIndex

Q: Can LlamaIndex filter which Actionstep MCP tools execute?

You apply the allowedtools parameter during setup. This restricts your application to specific read-only operations if needed.

Index your Actionstep case files into LlamaIndex for semantic legal search.

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Connect Actionstep MCP to LlamaIndex

Create your Vinkius account to connect Actionstep 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 Actionstep with LlamaIndex

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Index matters with this MCP Server

This MCP Server provides `list_matters` and `get_matter_details` to feed your vector stores. Your application pulls live case files and converts them into searchable nodes. Instead of guessing facts, your RAG setup queries actual firm data. Users ask questions, and the system retrieves grounded answers based on real legal records rather than generic training weights.

Embed task lists and notes

Extracting daily workflow context requires `list_legal_tasks` and `list_matter_notes`. The framework embeds these text blocks directly into your unified knowledge base. Lawyers search for a specific client discussion and get the exact file note back. Passing the include_resources argument forces your index to capture the full depth of the text.

Query billable hours semantically

Bringing financial context into your application starts with `list_time_entries`. You map out who worked on what without writing SQL queries. The FunctionAgent decides when to refresh this specific index chunk. If someone asks about yesterday's billing, the agent pulls the fresh data and synthesizes a direct response.

Setup guide

Set up Actionstep 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 Actionstep 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 Actionstep tools.",
)
response = await agent.run("List recent Actionstep data")

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Common questions about Actionstep MCP in LlamaIndex

Initialize BasicMCPClient with your Vinkius URL. Wrap it in McpToolSpec and call the async tool list method to expose the operations to your agent.

You apply the allowed_tools parameter during setup. This restricts your application to specific read-only operations if needed.

No, because it grounds responses in the actual API returns. The system reads the JSON response before generating any text.

You trigger a re-index of the specific tool output. The vector store updates the embeddings for those modified file notes.

The Vinkius platform handles authentication via a single endpoint token within a hardened V8 sandbox. Your sensitive matter details and contact lists pass through this ephemeral environment without ever being logged or stored persistently.

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