How to Use the Trefle MCP in CrewAI

Q: How do I get a comprehensive list of available plant types using Trefle and CrewAI?

You'll use listspecies to pull all species, including sub-taxa. Another agent can then run listgenera to map out the high-level groups for context.

Q: If I need a comprehensive list of all distributions using Trefle and CrewAI?

Start by calling listdistributions. This provides the complete catalog of zones, which you can then pass to other agents for targeted plant searches.

Q: How do I get basic data about a known species using Trefle and CrewAI?

Use getspecies or simply call listplants. The latter is better if you just want the main roster of plant names, excluding lower-level variations.

Coordinate botanical research with Trefle using CrewAI multi-agent teams.

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Setup Trefle with CrewAI

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Search Botanical Records Across the Board

The `search_plants` tool lets your crew search through all main plant species. Assign one agent to this task, and it quickly narrows down thousands of entries using keyword matching. For broader searches involving sub-taxa, another agent can use `search_species`. You assign roles—one researches general plants, the other tackles detailed species taxonomy.

Define Plant Distribution Zones

Need to know what's native to a region? The `get_distribution_plants` tool requires you to specify parameters like 'native' establishments. A monitoring agent can use this to validate the scope of research. This allows your multi-agent crew to perform geo-targeted analysis, checking specific distribution zones against known plant life.

Access Core Taxonomic Details

The `get_genus` and `get_species` tools retrieve core details for a given taxonomic level. A specialized agent can handle this by first identifying the genus, then pulling all associated species data. This structured approach ensures that your crew builds its understanding of the flora systematically, one level at a time.

Setup guide

Set up Trefle MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Trefle tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Trefle Analyst",
    goal="Access and analyze Trefle data via MCP.",
    backstory="Expert analyst with direct Trefle access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Trefle transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Trefle Analyst",
        goal="Access and analyze Trefle data via MCP.",
        backstory="Expert analyst with direct Trefle access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Trefle transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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.

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Common questions about Trefle MCP in CrewAI

You'll use `list_species` to pull all species, including sub-taxa. Another agent can then run `list_genera` to map out the high-level groups for context.

Yes, your crew can manage data quality by invoking tools like `report_species_error`. This lets a dedicated 'QA Agent' submit findings or flag bad data points for human review.

The MCP Server deals with objective botanical information: plant names, species lists, and distribution zone coordinates. It has no access to private user or personal data.

Start by calling `list_distributions`. This provides the complete catalog of zones, which you can then pass to other agents for targeted plant searches.

Use `get_species` or simply call `list_plants`. The latter is better if you just want the main roster of plant names, excluding lower-level variations.

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