How to Use the Eden AI MCP in LlamaIndex

Q: Can LlamaIndex index the output of Eden AI tools?

Yes, you can load tool outputs like listaiworkflows directly into a LlamaIndex vector index. This allows your RAG pipeline to query your active Eden AI workflows using semantic search.

Q: How does LlamaIndex use Eden AI to optimize model selection?

Your agent queries listallllmmodels via the Eden AI MCP server and indexes the results. When a query comes in, LlamaIndex checks the index to route the task to the most cost-effective model.

Q: Can I build a billing dashboard in LlamaIndex using Eden AI?

Yes, by indexing the output of getapiusagestatistics and getedenaimetadata. Your users can then query their Eden AI account credit balance and usage history using natural language in LlamaIndex.

Q: How is my Eden AI billing data protected in LlamaIndex?

All sensitive billing data and usage logs retrieved via getapiusagestatistics are processed locally within your secure sandbox. Vinkius handles the underlying Eden AI authentication tokens, keeping your credentials hidden from the LlamaIndex LLM.

Index your Eden AI workflow configurations and provider metrics directly into LlamaIndex vector stores.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Eden AI MCP to LlamaIndex

Create your Vinkius account to connect Eden AI 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 Eden AI with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index live Eden AI configurations for LlamaIndex RAG

Stop guessing which Eden AI workflow is active in your LlamaIndex app. Your LlamaIndex agent can run `list_ai_workflows` and index the raw JSON output directly into your vector store using this MCP Server. The LlamaIndex agent uses `get_workflow_configuration` to pull detailed steps of a specific Eden AI pipeline into the index. When a user asks how a task is processed, your LlamaIndex system answers using live Eden AI configuration data.

Semantic search over Eden AI metrics in LlamaIndex

Turn raw Eden AI API data into searchable LlamaIndex knowledge. By querying `get_api_usage_statistics` and `get_eden_ai_metadata`, your LlamaIndex pipeline builds a real-time index of your Eden AI account's credit balance and spending patterns. Your LlamaIndex agent can then answer natural language questions about your Eden AI billing. If you ask "which Eden AI models cost us the most?", the LlamaIndex agent queries the index containing your latest usage statistics to give an accurate, grounded answer.

Build a dynamic Eden AI model registry in LlamaIndex

This MCP Server lets your LlamaIndex agent query `list_all_llm_models` to fetch the current list of available models across all major Eden AI providers. The LlamaIndex agent indexes these Eden AI options to help users select the right model. You can combine this with `list_available_ai_features` to map out which Eden AI subfeatures are supported inside LlamaIndex. The LlamaIndex index updates dynamically, so your RAG application always knows which Eden AI models are currently available.

Setup guide

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

Yes, you can load tool outputs like `list_ai_workflows` directly into a LlamaIndex vector index. This allows your RAG pipeline to query your active Eden AI workflows using semantic search.

Your agent queries `list_all_llm_models` via the Eden AI MCP server and indexes the results. When a query comes in, LlamaIndex checks the index to route the task to the most cost-effective model.

Yes, by indexing the output of `get_api_usage_statistics` and `get_eden_ai_metadata`. Your users can then query their Eden AI account credit balance and usage history using natural language in LlamaIndex.

No, the LlamaIndex MCP tool spec handles the schema conversion automatically. The Eden AI tool outputs are returned in clean JSON formats that LlamaIndex can index or pass to a LLM without extra parsing.

All sensitive billing data and usage logs retrieved via `get_api_usage_statistics` are processed locally within your secure sandbox. Vinkius handles the underlying Eden AI authentication tokens, keeping your credentials hidden from the LlamaIndex LLM.

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