How to Use the Watershed Climate MCP in LlamaIndex

Q: How does LlamaIndex help me query past emissions data from Watershed Climate?

LlamaIndex indexes the output of tools like listinventories or getreport. Instead of just receiving a file, you build a knowledge base where API data is part of the index, allowing semantic search across historical reports.

Q: Can I track my progress against goals using Watershed Climate and LlamaIndex?

Yes. You can use the MCP Server's listreductiontargets to gather all defined goals. Indexing these targets lets you answer queries like, 'What is our target reduction for Scope 3 by 2030?' based on stored API data.

Q: How do I check the status of a complex submission with Watershed Climate and LlamaIndex?

The gettaskstatus tool is key here. After calling submitupload, you get a task ID. You can then query the MCP Server repeatedly using this ID to monitor progress, ensuring your RAG application knows when data processing is finished.

Index Emissions Data with LlamaIndex: Turn Watershed Climate Outputs into Searchable Knowledge.

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Connect Watershed Climate MCP to LlamaIndex

Create your Vinkius account to connect Watershed Climate 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 Watershed Climate with LlamaIndex

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Access Full Report Details

Use `get_report` to access full report details, including generated files and disclosure frameworks covered. This requires you first calling `list_reports` to find the correct report ID. Reports are usually created after inventories are complete. The process is: data -> inventory -> report. LlamaIndex helps ground your RAG application in these final, validated findings.

Monitor Inventory Status

Call `get_inventory` to get detailed information about a specific GHG inventory based on its ID. This tool shows the full composition of your organization's emissions. When you need to check overall organizational data, start by calling `list_inventories`. LlamaIndex indexes these results so you can query past years or different scope breakdowns semantically.

Manage Upload Containers

To ingest activity data, use `create_upload` to make a new container. This upload acts as the batch grouping mechanism for all related activities. Once the container exists, you can list its contents using `list_uploads`, or view individual records with `list_upload_data_records`. LlamaIndex allows you to build a knowledge base around this operational data.

Setup guide

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

LlamaIndex indexes the output of tools like `list_inventories` or `get_report`. Instead of just receiving a file, you build a knowledge base where API data is part of the index, allowing semantic search across historical reports.

You combine tool calls into an RAG application. For instance, you might query `list_reports` results and then pass those IDs to a document retriever that feeds the content into the index for deep analysis.

Yes. You can use the MCP Server's `list_reduction_targets` to gather all defined goals. Indexing these targets lets you answer queries like, 'What is our target reduction for Scope 3 by 2030?' based on stored API data.

The `get_task_status` tool is key here. After calling `submit_upload`, you get a task ID. You can then query the MCP Server repeatedly using this ID to monitor progress, ensuring your RAG application knows when data processing is finished.

The server touches activity data records and emissions measurements. These are the specific values that get processed into CO2e calculations, forming the core data set managed by Watershed Climate.

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