How to Use the Kong (AI API Gateway) MCP in LlamaIndex

Q: How do I configure LlamaIndex with the Kong (AI API Gateway) MCP Server?

Install llama-index-tools-mcp and initialize the basic MCP client. Wrap it in McpToolSpec and convert it to a tool list to pass to your FunctionAgent.

Q: Can I use LlamaIndex to find unused routes in my Kong (AI API Gateway)?

Yes, you can index the outputs of listroutes and listservices into a vector store. Your agent can then run semantic queries to identify routes that do not map to any active upstream service.

Q: How does LlamaIndex semantic search improve Kong (AI API Gateway) plugin management?

By indexing the output of listplugins, your agent can quickly find which services are running specific rate-limiting or AI proxy configurations. This eliminates the need to write complex custom scripts to parse JSON payloads.

Q: How does the Kong (AI API Gateway) MCP Server protect my gateway admin credentials?

All administration calls and credential data generated via createconsumerkey are executed inside a secure, ephemeral V8 sandbox. Your sensitive admin tokens are never indexed or stored in your external vector databases.

Index your Kong (AI API Gateway) configurations and routing tables into LlamaIndex vector stores for RAG-driven gateway control.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Kong (AI API Gateway) MCP to LlamaIndex

Create your Vinkius account to connect Kong (AI API Gateway) 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.

GDPR Free for Subscribers

Setup Kong (AI API Gateway) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build a searchable knowledge base of your Kong gateway

Index your active API endpoints into vector stores by calling `list_services` and `list_routes`. LlamaIndex ingests your gateway's routing topology directly into a vector store. You can query your system to find orphan services or misconfigured paths without manual audits. This MCP Server provides the raw data feed your RAG pipeline needs. Instead of digging through config files, you ask your agent which plugins are active, and it queries the indexed vector store to give you a grounded, hallucination-free answer.

Ground your LlamaIndex agent in real-time Kong state

Prevent routing conflicts by checking your current setup with `list_services` before executing any edits. This checks your vector database to see if a similar service already exists before creating new routes with `create_route`. It prevents duplicate routes and keeps your gateway configuration clean. If a service configuration is modified, your agent triggers `update_plugin` and immediately re-indexes the new state. This keeps your runtime environment and your agent's knowledge base perfectly synchronized.

Document and audit plugin deployments with RAG

Audit your gateway's security profiles by querying active configurations with `list_plugins`. Your agent uses this to pull active configurations and maps them to specific consumers retrieved via `list_consumers`. The output is formatted and indexed, allowing you to ask complex questions like which consumers have access to specific LLM models. This turns raw gateway state into an interactive, natural-language documentation hub.

Setup guide

Set up Kong (AI API Gateway) 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 Kong (AI API Gateway) 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 Kong (AI API Gateway) tools.",
)
response = await agent.run("List recent Kong (AI API Gateway) data")

Get your connection token →

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

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Kong (AI API Gateway) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Kong (AI API Gateway) MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the basic MCP client. Wrap it in `McpToolSpec` and convert it to a tool list to pass to your `FunctionAgent`.

Yes, you can index the outputs of `list_routes` and `list_services` into a vector store. Your agent can then run semantic queries to identify routes that do not map to any active upstream service.

By indexing the output of `list_plugins`, your agent can quickly find which services are running specific rate-limiting or AI proxy configurations. This eliminates the need to write complex custom scripts to parse JSON payloads.

Yes, your LlamaIndex agent can execute write operations like `create_ai_plugin` or `delete_plugin` and immediately update the local vector index with the new state. This ensures your search index remains accurate.

All administration calls and credential data generated via `create_consumer_key` are executed inside a secure, ephemeral V8 sandbox. Your sensitive admin tokens are never indexed or stored in your external vector databases.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript