How to Use the ComboCurve MCP in LlamaIndex

Index your field data into LlamaIndex for grounded, accurate RAG responses.

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Connect ComboCurve MCP to LlamaIndex

Create your Vinkius account to connect ComboCurve 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 ComboCurve with LlamaIndex

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Perplexity

Ground your LlamaIndex knowledge base

Use `list_projects` to identify relevant data sets for your index. Once retrieved, the results of `get_project` populate your vector store with live operational facts. This keeps your RAG application accurate. Your agent stops guessing because it queries the actual production data stored in the index.

Build searchable well repositories

Run `list_wells` to gather broad sets of asset information for your index. You can then perform semantic search across your entire well catalog using LlamaIndex. When you need specific details, `get_well` fetches the latest stats. Your agent uses this to provide precise answers based on current field performance.

Automate forecast data retrieval

The `get_forecast` and `get_monthly_volumes` tools allow your agent to fetch time-series data for your RAG pipeline. Your index stays updated with the latest figures. This eliminates stale information. Your agent queries the index, which reflects the most recent state of your project forecasts.

Setup guide

Set up ComboCurve 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 ComboCurve 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 ComboCurve tools.",
)
response = await agent.run("List recent ComboCurve data")

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Common questions about ComboCurve MCP in LlamaIndex

You convert the tools into a specification that the framework uses to populate your index. This allows your agent to retrieve and store API results as part of your persistent knowledge base.

You can apply filters to the tool list before passing them to your agent. This restricts the scope of what the model can index or query from the API.

The server acts as a gateway that only exposes the data your agent explicitly requests. Your sensitive well logs are not stored anywhere outside your controlled index environment.

Your agent can re-run the relevant tool to refresh the index. This ensures that any subsequent queries reflect the updated forecast volumes.

Yes, you can use the built-in adapters to transform these calls into searchable documents. This turns your API data into a grounded reference for all future AI responses.

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