How to Use the Trefle MCP in LlamaIndex

Q: How do I search plant data using Trefle and LlamaIndex?

You use searchplants or searchspecies. LlamaIndex takes the resulting structured data and indexes it, allowing you to query across the entire dataset semantically. You get answers grounded in actual API results.

Q: Can I track genus information using Trefle and LlamaIndex?

Yes. The listgenera tool provides a list of all available genera, and the output is indexed for easy retrieval. You can then query across multiple genera without needing to rerun the API call.

Q: What's the best way to get distribution data with Trefle and LlamaIndex?

Use getdistributionplants after filtering by an establishment. The resulting geographical data is indexed, letting you query patterns or overlaps across different zones later on.

Q: Does Trefle support submitting corrections with LlamaIndex?

The system supports submitting corrections via submitspeciescorrection. This tool's output can then be stored and indexed, creating a historical log of all requested data changes.

Index botanical findings from Trefle into vector stores using LlamaIndex.

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Connect Trefle MCP to LlamaIndex

Create your Vinkius account to connect Trefle 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 Trefle with LlamaIndex

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Indexing Tool Outputs

The `get_plant` tool retrieves specific plant details. When you index this data, the output becomes a searchable chunk of knowledge in your vector store. Your agent can then query past session results or live API data using semantic search. This means you don't just get an answer; you build a unified, queryable index combining your documents and real-time botanical records.

Deep Species Retrieval

You can use `search_species` to look through every available species and sub-taxa. LlamaIndex indexes these results, allowing you to query conceptually—for example, 'show me everything related to wetland aquatic plants'—rather than just exact keywords. This process transforms raw API calls into high-density, retrievable knowledge chunks.

Comprehensive Botanical Search

Run `search_plants` against the main plant species database. The results from this tool are indexed immediately, creating a persistent record of what you looked up. You can then query that index later to answer questions based on live data access. It's ideal for building RAG applications where API calls ground your knowledge base.

Setup guide

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

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Common questions about Trefle MCP in LlamaIndex

You use `search_plants` or `search_species`. LlamaIndex takes the resulting structured data and indexes it, allowing you to query across the entire dataset semantically. You get answers grounded in actual API results.

Yes. The `list_genera` tool provides a list of all available genera, and the output is indexed for easy retrieval. You can then query across multiple genera without needing to rerun the API call.

Use `get_distribution_plants` after filtering by an establishment. The resulting geographical data is indexed, letting you query patterns or overlaps across different zones later on.

The system supports submitting corrections via `submit_species_correction`. This tool's output can then be stored and indexed, creating a historical log of all requested data changes.

This MCP Server exposes structured botanical data types: plant names, species records, genus details, and geographical distribution zones. All outputs are text-based JSON structures.

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