How to Use the Upstream Lens MCP in LlamaIndex

Q: How can LlamaIndex use Upstream Lens for API health checks?

You feed the output of checkapihealth into your index. This allows you to build a knowledge base that tracks historical connection status, so future queries are grounded in past operational data.

Q: Can LlamaIndex help query property metadata?

Yes. Index the output from getorganizationinfo and then use RAG to ask natural language questions about corporate structure or asset ownership, getting answers rooted in Upstream Lens data.

Q: What data types are touched by Upstream Lens and LlamaIndex regarding privacy?

This server handles environmental project metadata (from getorganizationinfo) and the raw observational text found in listprojectnotes. Both types of information can be indexed for retrieval.

Q: Can I build a comprehensive guide using Upstream Lens and LlamaIndex?

You index results from all tools—like listportfolios and listprojectnotes. This creates one unified, queryable knowledge base that combines structured data with free-text reports.

Q: Does Upstream Lens and LlamaIndex support viewing multiple site lists?

You index the outputs of both listprojects (all environmental projects) and listportfolios. This lets your agent answer questions that require cross-referencing two distinct types of asset data.

Ground your answers in oil and gas data using LlamaIndex with Upstream Lens.

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Connect Upstream Lens MCP to LlamaIndex

Create your Vinkius account to connect Upstream Lens 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 Upstream Lens with LlamaIndex

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Build a searchable knowledge base from the MCP Server.

When you call `list_projects`, index that list immediately. Later, when asked about 'environmental sites,' your LlamaIndex agent won't hallucinate; it pulls the exact project IDs from its vector store. This means your RAG application is always grounded in real API data.

Query property details for contextual answers.

Need to know about a specific feature? Use `get_property_details`. By indexing these results, you can ask complex questions like, 'What are the specs of all properties in this region?' and get an answer synthesized from multiple data points. The output becomes part of your knowledge base for future queries.

Track project history with indexed observations.

`list_project_observations` generates detailed records. Indexing these means you can query past sessions—'What did we observe about Site X last quarter?'—and get the factual answer instantly, without needing to manually parse large JSON blobs. It makes historical data immediately searchable by semantic meaning.

Setup guide

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

You feed the output of `check_api_health` into your index. This allows you to build a knowledge base that tracks historical connection status, so future queries are grounded in past operational data.

Yes. Index the output from `get_organization_info` and then use RAG to ask natural language questions about corporate structure or asset ownership, getting answers rooted in Upstream Lens data.

This server handles environmental project metadata (from `get_organization_info`) and the raw observational text found in `list_project_notes`. Both types of information can be indexed for retrieval.

You index results from all tools—like `list_portfolios` and `list_project_notes`. This creates one unified, queryable knowledge base that combines structured data with free-text reports.

You index the outputs of both `list_projects` (all environmental projects) and `list_portfolios`. This lets your agent answer questions that require cross-referencing two distinct types of asset data.

Use it with your favorite AI tools

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