AEGIS Hedging MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AEGIS Hedging 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 AEGIS Hedging. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in AEGIS Hedging?"
)
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 AEGIS Hedging MCP Server
Connect your AEGIS Hedging (Revenue Intelligence) account to your AI agent to orchestrate your energy risk management and trade lifecycle. From monitoring real-time Mark-to-Market (MTM) valuations to auditing trade entries and retrieving forward market curves, your agent handles complex energy data through natural conversation.
LlamaIndex agents combine AEGIS Hedging tool responses with indexed documents for comprehensive, grounded answers. Connect 4 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
- Trade Lifecycle Management — List and audit trades executed on the AEGIS Marketplace and monitor their status in your system
- Valuation & MTM — Retrieve current Mark-to-Market data and settlement estimates for your hedge positions
- Market Intelligence — Access energy forward curves, historical price data, and daily market insights
- Reporting & Compliance — Retrieve normalized data for revenue, taxes, and fees to support financial forecasting and SDR reporting
- Strategic Oversight — Quickly identify portfolio risks or hedging opportunities based on current market volatility
The AEGIS Hedging MCP Server exposes 4 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 AEGIS Hedging to LlamaIndex via MCP
Follow these steps to integrate the AEGIS Hedging 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 4 tools from AEGIS Hedging
Why Use LlamaIndex with the AEGIS Hedging MCP Server
LlamaIndex provides unique advantages when paired with AEGIS Hedging through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AEGIS Hedging tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AEGIS Hedging tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AEGIS Hedging, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AEGIS Hedging tools were called, what data was returned, and how it influenced the final answer
AEGIS Hedging + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AEGIS Hedging MCP Server delivers measurable value.
Hybrid search: combine AEGIS Hedging real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AEGIS Hedging 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 AEGIS Hedging for fresh data
Analytical workflows: chain AEGIS Hedging queries with LlamaIndex's data connectors to build multi-source analytical reports
AEGIS Hedging MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect AEGIS Hedging to LlamaIndex via MCP:
check_api_version
Check AEGIS API version
get_forward_curves
Retrieve energy forward curves
get_valuations
Get real-time MTM valuations
list_trades
List energy hedge trades
Example Prompts for AEGIS Hedging in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AEGIS Hedging immediately.
"List all energy trades executed in the last 30 days."
"Show me the current Mark-to-Market valuation for my natural gas portfolio."
"Retrieve the energy forward curves for the next 12 months."
Troubleshooting AEGIS Hedging MCP Server with LlamaIndex
Common issues when connecting AEGIS Hedging to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAEGIS Hedging + LlamaIndex FAQ
Common questions about integrating AEGIS Hedging 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 AEGIS Hedging 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 AEGIS Hedging to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.