How to Use the Factorial MCP in LlamaIndex

Q: How do I restrict Factorial tools in LlamaIndex?

Use the allowedtools filter during the tool spec initialization.

Index your HR data into LlamaIndex for semantic search and grounded answers across your entire company knowledge base.

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Connect Factorial MCP to LlamaIndex

Create your Vinkius account to connect Factorial 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 Factorial with LlamaIndex

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Semantic indexing for LlamaIndex

Turn `list_documents` and `list_folders` output into searchable vectors. You query your internal HR knowledge just like a document store. Your agent retrieves context from these indexes rather than relying on stale info. It grounds responses in live company data.

Grounding agents with Factorial

Combine `list_holidays` and `list_employees` with your existing knowledge base. LlamaIndex creates a unified index that maps HR policies to actual employee records. You stop guessing about holiday dates or team structures. The agent provides answers based on the latest API state.

Filtered tool access in LlamaIndex

Control exactly which tools the agent sees. You restrict access to `list_payslips` while allowing broad search across `list_teams`. The tool spec allows for granular permissions. You define the boundary so the agent only interacts with the data you authorize.

Setup guide

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

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