How to Use the Metatext MCP in CrewAI

Deploy specialized NLP agents with Metatext on CrewAI for autonomous research and analysis.

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Connect Metatext MCP to CrewAI

Create your Vinkius account to connect Metatext to CrewAI 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 Metatext with CrewAI

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Perplexity

Collaborative NLP Teams

Assign `list_nlp_models` to your researcher agent to identify the right tool for the job. Another agent then executes the task. Each agent has a specific scope. This keeps your operation clean and prevents one agent from doing too much.

Autonomous Dataset Ops

Let your moderator agent use `list_dataset_records` to monitor incoming data. It flags anomalies for human review. You avoid manual oversight. The agent acts on the data patterns it finds in real time.

Multi-Agent Inference

Configure your agents to use `run_model_inference` for complex analysis. One agent prepares the input, the other processes the result. It works sequentially. The crew manages the handoff between agents so you get a finished result.

Setup guide

Set up Metatext 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 Metatext tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Metatext 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 →

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="Metatext Analyst",
        goal="Access and analyze Metatext data via MCP.",
        backstory="Expert analyst with direct Metatext access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Metatext 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 Metatext. 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 Metatext MCP in CrewAI

Yes. By using shared memory, your agents can pass the results of a tool call to one another for subsequent steps.

Use the tool filter in your MCP setup. This gives each agent access to only the tools they need for their assigned role.

It will. You can grant the manager agent broader tool access while limiting the worker agents to specific execution tools.

Your data is handled via secure transport. The MCP server only touches the specific NLP records you authorize through your tool calls.

The agent framework logs all interaction history. You can audit every action taken by the crew against your data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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