Eden AI MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Eden AI as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Eden AI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Eden AI?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Eden AI MCP Server
Integrate Eden AI, the unified AI API platform, directly into your AI workflow. Manage your automation workflows and pipelines, track available AI providers (OpenAI, Google, AWS, etc.) across various features, monitor real-time API usage and costs, and oversee your LLM models using natural language.
LlamaIndex agents combine Eden AI tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Workflow Oversight — List and retrieve detailed information and status for all your configured AI automation workflows.
- Provider Intelligence — Access the provider registry to monitor available AI capabilities, pricing, and service levels for specific features.
- Usage Monitoring — Track real-time API consumption statistics, credit balance, and organizational spending across all providers.
- Model Management — List all specific large language models (LLMs) and AI features supported by the Eden AI platform.
The Eden AI MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Eden AI to LlamaIndex via MCP
Follow these steps to integrate the Eden AI MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Eden AI
Why Use LlamaIndex with the Eden AI MCP Server
LlamaIndex provides unique advantages when paired with Eden AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Eden AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Eden AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Eden AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Eden AI tools were called, what data was returned, and how it influenced the final answer
Eden AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Eden AI MCP Server delivers measurable value.
Hybrid search: combine Eden AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Eden AI to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Eden AI for fresh data
Analytical workflows: chain Eden AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Eden AI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Eden AI to LlamaIndex via MCP:
get_ai_feature_pricing
Identify the pricing for a specific AI feature across different providers
get_api_usage_statistics
Retrieve technical statistics on your API usage and costs
get_eden_ai_metadata
Retrieve metadata and credit balance for your Eden AI account
get_workflow_configuration
Get detailed settings and steps for a specific AI workflow
list_ai_providers
List all AI providers (OpenAI, Google, AWS, etc.) available for a specific feature
list_ai_workflows
List all AI automation workflows configured in your Eden AI account
list_all_llm_models
List all specific large language models available through the unified API
list_available_ai_features
List all AI features and subfeatures supported by the Eden AI platform
list_latest_ai_automations
Identify the most recently updated AI workflows
quick_ai_provider_audit
Retrieve a high-level summary of available providers for text analysis
Example Prompts for Eden AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Eden AI immediately.
"List all active AI workflows."
"Show me the pricing for 'sentiment_analysis' across providers."
"What is my current Eden AI credit balance?"
Troubleshooting Eden AI MCP Server with LlamaIndex
Common issues when connecting Eden AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpEden AI + LlamaIndex FAQ
Common questions about integrating Eden AI MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Eden AI with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Eden AI to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.