How to Use the Cacheflow MCP in LlamaIndex

Q: Can I filter Cacheflow tools in LlamaIndex?

You can use the allowedtools filter to restrict which functions the agent accesses. This keeps your sales workflow focused.

Q: Does Cacheflow support LlamaIndex async operations?

Absolutely. Use the totoollistasync method to handle data retrieval without blocking your main process.

Index your sales data into LlamaIndex using the Cacheflow MCP server for grounded, accurate retrieval.

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Connect Cacheflow MCP to LlamaIndex

Create your Vinkius account to connect Cacheflow 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 Cacheflow with LlamaIndex

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Query Cacheflow data using LlamaIndex

Connect the server to `FunctionAgent` and let it index your proposal history. This creates a searchable knowledge base of your sales performance. Stop relying on guesses. Your agent retrieves actual `get_proposal_details` output from the vector store to answer questions about specific deals.

Build RAG pipelines with Cacheflow

Extract customer lists using `list_customers` and store them directly in your index. This keeps your agent current on who you are selling to. LlamaIndex treats these outputs as ground truth. When you ask about a client, the agent pulls from the latest API data rather than stale documents.

Automate approval tracking in LlamaIndex

Run `get_approval_requests` to see what is waiting on your desk. The agent summarizes pending items based on the indexed server responses. This turns your static sales data into a dynamic assistant. You get a clear view of your pipeline without manual searching.

Setup guide

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

The server returns structured output that your agent feeds into the vector store. This creates a searchable map of your sales pipeline.

No. The agent uses the indexed tool outputs as the primary source of truth. It only reports what it finds in your current sales records.

You can use the allowed_tools filter to restrict which functions the agent accesses. This keeps your sales workflow focused.

Absolutely. Use the `to_tool_list_async` method to handle data retrieval without blocking your main process.

Your proposal and customer data stay within the ephemeral sandbox provided by Vinkius. We use scoped tokens to ensure only your specific agent can touch your records.

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