How to Use the Cloudflare MCP in LlamaIndex

Q: How do I index Cloudflare KV store data into LlamaIndex?

The agent calls listkvnamespaces and listkvkeys via the MCP Server to discover keys. It then pulls raw JSON values using getkvkey and adds them to your LlamaIndex document store.

Q: Can LlamaIndex query Cloudflare D1 database structures?

Yes, the agent runs schema queries via queryd1 after listing databases with listd1databases. It indexes the resulting tables for semantic search.

Q: How do I audit Cloudflare Worker versions using LlamaIndex?

The agent fetches deployment logs via listworkerversions and metadata from getworkerversion via the MCP Server. LlamaIndex parses these version histories to find anomalous or outdated deployments.

Q: Can I check storage bucket locations?

Yes, the agent uses listr2buckets to retrieve bucket names and creation dates. This metadata is indexed to help your agent locate where files are stored.

Index Cloudflare Worker configurations, KV keys, and D1 schemas into LlamaIndex to query your edge network with RAG.

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Connect Cloudflare MCP to LlamaIndex

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Setup Cloudflare with LlamaIndex

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Build a searchable index of Cloudflare storage in LlamaIndex

`list_kv_namespaces` retrieves your key-value stores using this MCP Server so LlamaIndex can index your edge data structure. The agent calls `list_kv_keys` to map out active keys and reads their raw JSON values using `get_kv_key`. Your RAG pipeline indexes this metadata alongside bucket structures from `list_r2_buckets`. This lets your agent answer complex questions about your storage layout without hardcoding paths.

Monitor Cloudflare performance using LlamaIndex RAG

`get_worker_analytics` pulls execution counts and error rates to feed live performance data into your LlamaIndex knowledge base. The agent cross-references these metrics with DNS logs using `get_zone_analytics` to detect traffic spikes. By indexing these metrics, your agent correlates edge performance with application state. It uses `list_zones` to map analytics back to specific domains automatically.

Audit Cloudflare configurations with LlamaIndex

`list_worker_routes` fetches all active URL patterns so LlamaIndex can map out your edge routing architecture. The agent combines this routing map with script details from `get_worker` to identify orphaned endpoints. To verify deployment history, the agent reads code snapshots using `list_worker_versions` and audits changes. This builds a local index of your deployments via the MCP Server, making it easy to query who deployed what and when.

Setup guide

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

The agent calls `list_kv_namespaces` and `list_kv_keys` via the MCP Server to discover keys. It then pulls raw JSON values using `get_kv_key` and adds them to your LlamaIndex document store.

Yes, the agent runs schema queries via `query_d1` after listing databases with `list_d1_databases`. It indexes the resulting tables for semantic search.

The agent fetches deployment logs via `list_worker_versions` and metadata from `get_worker_version` via the MCP Server. LlamaIndex parses these version histories to find anomalous or outdated deployments.

Yes, the agent uses `list_r2_buckets` to retrieve bucket names and creation dates. This metadata is indexed to help your agent locate where files are stored.

Your data is fetched locally through the Vinkius MCP Server sandbox using your own API credentials. The retrieved database query results and key-value pairs are processed in your local memory and never stored on any external MCP registry.

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