How to Use the AEGIS Hedging MCP in LlamaIndex

Q: How do I index AEGIS Hedging trade data into LlamaIndex?

You use the McpToolSpec adapter to convert the listtrades tool into a LlamaIndex-compatible tool. The framework then reads the tool outputs directly into your document store for indexing.

Q: Does the AEGIS Hedging MCP Server support semantic search in LlamaIndex?

The server provides raw data like getforwardcurves, which LlamaIndex then embeds into vector space. This allows your agent to perform semantic searches over live energy market trends.

Q: Can LlamaIndex agents filter AEGIS Hedging tools?

Yes, you configure the allowedtools filter during client initialization. This restricts the agent to specific tools like getvaluations while ignoring administrative tools.

Q: How do I check the server connection before indexing?

You invoke checkapiversion to confirm your credentials and endpoint status. Running this check first prevents indexing errors during bulk data loads.

Index live AEGIS Hedging curves and trade valuations directly into your LlamaIndex vector stores.

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Create your Vinkius account to connect AEGIS Hedging 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.

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Setup AEGIS Hedging with LlamaIndex

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Index energy curves in LlamaIndex

The `get_forward_curves` tool pulls raw energy pricing models for your LlamaIndex ingestion pipeline. Once retrieved, the framework indexes these curves into vector storage, turning flat market data into a searchable knowledge base. Your RAG pipeline queries this vector store to find historical pricing patterns. This setup eliminates manual data parsing by letting your LlamaIndex agent match current trends against indexed market curves.

Query trade data using this MCP Server

To pull active energy positions, the `list_trades` tool integrates directly with your LlamaIndex query engine via the MCP protocol. Your agent searches these trades semantically to resolve complex portfolio questions instantly. By feeding this output to a LlamaIndex FunctionAgent, you ground your agent's answers in actual trade records. This mechanism prevents the agent from hallucinating transactional details.

Index live valuations in LlamaIndex

By delivering real-time mark-to-market data, the `get_valuations` tool updates your LlamaIndex document store. The framework automatically parses these numbers, updating your semantic index with current financial realities. Your LlamaIndex agent references these indexed valuations during complex financial synthesis. You get accurate, context-aware risk reports because the data matches your actual live positions.

Setup guide

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

You use the McpToolSpec adapter to convert the `list_trades` tool into a LlamaIndex-compatible tool. The framework then reads the tool outputs directly into your document store for indexing.

The server provides raw data like `get_forward_curves`, which LlamaIndex then embeds into vector space. This allows your agent to perform semantic searches over live energy market trends.

Yes, you configure the allowed_tools filter during client initialization. This restricts the agent to specific tools like `get_valuations` while ignoring administrative tools.

You invoke `check_api_version` to confirm your credentials and endpoint status. Running this check first prevents indexing errors during bulk data loads.

Your energy hedge trades and market valuations reside solely in your local or cloud vector database. Vinkius operates as a zero-trust gateway, passing the data securely without storing your proprietary financial records.

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