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How to Use the Trefle MCP in Mastra AI

Build complex botanical workflows with Trefle on Mastra AI.

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Works with every AI agent you already use

…and any MCP-compatible client

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Mastra AI

Connect Trefle MCP to Mastra AI

Create your Vinkius account to connect Trefle to Mastra AI 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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Get Specific Plant Details

The `get_plant` tool retrieves full details for a single, specific plant. You pass the name, and your agent returns all associated data points. This is ideal when your workflow needs to pull concrete facts about one item. It's designed for multi-step processes: find out what you need, then use that fact in the next step of your automated routine.

Search and Correct Taxonomy Records

If a species entry is wrong, don't stop the workflow. You can automatically route data through `report_species_error` or `submit_species_correction`. These tools allow your Mastra AI agent to handle necessary database upkeep. Or, if you just need information, use `search_plants` or `search_species` to quickly find records across the entire database.

Map Global Plant Distribution

Need to know what grows where? The `get_distribution_plants` tool filters the world's data by a zone. You specify criteria, like filtering for 'native' life forms, and get the results. This capability is perfect for complex conditional branching: IF the user specifies Zone X, THEN check which native plants are found there.

Setup guide

Set up Trefle MCP in Mastra AI

Prerequisites

  • Node.js 18+ and a TypeScript project
  • @mastra/mcp + @mastra/core packages
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install dependencies

    Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).

  2. 2

    Configure the MCPClient

    Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

  3. 3

    Discover and inject tools

    Call mcpClient.listTools() and spread the result into your agent's tools object. All Trefle tools become native Mastra tools.

  4. 4

    Run with any model

    Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts
import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "trefle-mcp-client",
  servers: {
    "trefle-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Trefle Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Trefle tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Trefle transactions"
);
console.log(result.text);

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Trefle. 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.

Why Choose Vinkius

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Real-time monitoring

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visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Trefle MCP in Mastra AI

Use `list_genera`. The tool provides a definitive list of every genus in the botanical records. This is useful for initializing a workflow that needs to iterate over major groups.
Yes, call `get_species` with the name you suspect. If data returns, the species is listed. It's simple verification that keeps your automated process moving.
The server primarily accesses botanical records: plant names, species details, and geographical distribution zones. There are no personal user identifiers involved in the tool calls.
The `list_plants` tool handles this by giving you a clean roster of species, while filtering out varieties or subspecies. This keeps your final output very focused.
You use `submit_species_correction`. Your agent sends the specific erroneous details to this endpoint, letting you build a workflow that handles continuous data quality checks.

Start using the Trefle MCP today

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