How to Use the Cloverly MCP in LlamaIndex

Q: Does LlamaIndex support Cloverly tool filtering?

Yes. You can restrict the agent to specific tools like listprojects to keep the context window clean. This ensures the model only accesses relevant environmental data.

Q: Will LlamaIndex hallucinate Cloverly stats?

By indexing the output of getimpactbytype, the AI relies on your actual data. It retrieves specific figures from your vector store instead of guessing.

Q: Can I search past Cloverly purchases using LlamaIndex?

Absolutely. By ingesting the results of listpurchases into your index, you can query for specific dates, project types, or offset amounts.

Index carbon data into LlamaIndex for RAG applications that answer questions about your environmental footprint.

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Connect Cloverly MCP to LlamaIndex

Create your Vinkius account to connect Cloverly 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 Cloverly with LlamaIndex

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Indexing Cloverly data in LlamaIndex

Transform carbon offset records into a searchable knowledge base. Use `list_purchases` to retrieve historical data and feed the results into your vector store. Your RAG application now treats carbon credit metadata as primary facts. This grounds your answers in actual API results rather than vague projections.

Retrieving impact data for LlamaIndex

Fetch granular impact reports using `get_impact_summary`. When your index contains these records, your agent performs semantic searches to explain your progress to stakeholders. This makes your historical environmental data queryable alongside internal documentation. It turns raw numbers into a narrative your team can actually understand.

Checking Cloverly connection health

Run `check_cloverly_status` as a tool within your LlamaIndex workflow to verify server availability. If the service is down, your agent can alert you before attempting complex indexing tasks. This creates a self-healing pipeline. Your agent handles the connectivity checks so you don't have to monitor the service manually.

Setup guide

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

Use the llama-index-tools-mcp library to wrap the server. Once the tools are converted to FunctionAgent compatible specs, they become part of your indexable knowledge.

Yes. You can restrict the agent to specific tools like `list_projects` to keep the context window clean. This ensures the model only accesses relevant environmental data.

By indexing the output of `get_impact_by_type`, the AI relies on your actual data. It retrieves specific figures from your vector store instead of guessing.

Absolutely. By ingesting the results of `list_purchases` into your index, you can query for specific dates, project types, or offset amounts.

Your API tokens stay within the Vinkius sandbox. We isolate all index operations, ensuring your carbon data remains private and only accessible via your authorized agent.

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