How to Use the Factor (Cofactr) MCP in LlamaIndex

Q: Can I filter which Factor (Cofactr) tools my LlamaIndex agent sees?

You can use the allowedtools filter to restrict access. This ensures your agent only uses the specific tools you want for a given task.

Index your Factor (Cofactr) data into LlamaIndex to build knowledge-heavy procurement agents that answer complex supply chain questions.

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Connect Factor (Cofactr) MCP to LlamaIndex

Create your Vinkius account to connect Factor (Cofactr) 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 Factor (Cofactr) with LlamaIndex

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Build searchable knowledge from Factor (Cofactr) data

Use `list_items` and `list_suppliers` to pull data directly into your vector index. Your agent no longer guesses at part availability; it queries a grounded knowledge base built on live API results. This turns static API outputs into a queryable asset. You get precise answers backed by current stock levels and supplier lead times, not hallucinations.

Combine docs and live API data in LlamaIndex

Merge your internal procurement PDFs with real-time data from `get_rfq`. Your agent can compare contract terms against current quote details in a single semantic search. It bridges the gap between your static documentation and dynamic operations. You'll have a unified view of your supply chain that updates as you query the server.

Execute complex procurement tasks with FunctionAgent

Use the `to_tool_list_async` function to hand off tools like `create_purchase_order` to your LlamaIndex agent. It manages the tool execution while maintaining the context of your index. This setup allows for advanced RAG patterns where the agent retrieves information to inform its next action. It's the right way to manage high-volume part lookups and order creation.

Setup guide

Set up Factor (Cofactr) 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 Factor (Cofactr) 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 Factor (Cofactr) tools.",
)
response = await agent.run("List recent Factor (Cofactr) 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 Factor (Cofactr). 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 Factor (Cofactr) MCP in LlamaIndex

The MCP tools return structured data that you can inject directly into your vector store. This makes every part and purchase order searchable by your agent.

You can use the allowed_tools filter to restrict access. This ensures your agent only uses the specific tools you want for a given task.

All API communication occurs over a secure, encrypted transport. Your procurement data is never stored on external servers and is only used to populate your local vector index.

You can refresh your index by re-running the tool calls. This keeps your agent's knowledge grounded in the most recent stock levels returned by the API.

Install the MCP tools package, initialize the client with your endpoint, and convert the tools to a list. From there, pass them to your agent and start querying your data.

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