How to Use the Legito MCP in LlamaIndex

Index your entire Legito document library into LlamaIndex for semantic search and grounded answers.

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Connect Legito MCP to LlamaIndex

Create your Vinkius account to connect Legito 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 Legito with LlamaIndex

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Search Legito records with LlamaIndex

Use `list_document_records` to pull data into your vector store. Your agent can then perform semantic searches across your existing files. This setup allows you to query specific document content using `get_document_version_data`. It grounds your AI agent in actual, up-to-date data.

Build RAG pipelines with this MCP Server

Connect your index to `list_template_suites` to keep your knowledge base current. The agent refreshes its context whenever you trigger an update. It effectively turns your document repo into a queryable database. You ask questions, and the agent fetches the specific version data.

Sync user permissions into your index

Access `list_users` and `get_user_permissions` to map your organizational structure. You can index these relationships to provide context-aware answers. It helps your agent understand who should see what. The server provides the raw data, and your index handles the retrieval.

Setup guide

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

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

Yes. You fetch the tool list, execute the calls, and feed the results into your vector store. It makes your documents searchable by intent.

You define a tool spec that wraps the server calls. The retriever triggers these tools to fetch fresh data before constructing an answer.

The speed depends on your document volume. We recommend indexing metadata first and fetching full version data only when the agent needs it.

You can filter tools using the client configuration. This limits the agent to only the operations you define.

We utilize scoped API keys for the connection. Every request to fetch document version data is logged, and only authorized users can trigger the indexing process.

Use it with your favorite AI tools

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