How to Use the Gradient AI (LLM API & Finetuning) MCP in CrewAI

Q: How do I add Gradient AI (LLM API & Finetuning) to my CrewAI agents?

Pass the server URL in the mcps list of your Agent definition. The agents will automatically register the tools and gain the ability to call them.

Q: How do I filter tools for Gradient AI (LLM API & Finetuning) in CrewAI?

Use the toolfilter option in the MCPServerHTTP class. This lets you restrict specific agents to only the tools they need for their role.

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Connect Gradient AI (LLM API & Finetuning) MCP to CrewAI

Create your Vinkius account to connect Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) with CrewAI

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Autonomous research and analysis with CrewAI

Assign one agent to `extract_pdf` and another to `summarize_document`. Your crew works together to turn raw documents into actionable insights without you needing to manage the process. The agents share memory, allowing them to pass context between tools. This turns your CrewAI operation into a self-contained research department.

Specialized training crews

Create a team where one agent prepares datasets via `upload_file` and another triggers `fine_tune_model`. This separates the data prep from the training execution. You can even add a moderator agent to verify the model output using `complete_model` before finalizing the training run.

Embedding generation at scale

Use your crew to iterate through large datasets and generate vectors using `generate_embeddings`. Your agents coordinate the batching to keep the pipeline moving. The results go straight into your collections. It’s an efficient way to build a searchable knowledge base using standard CrewAI patterns.

Setup guide

Set up Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Gradient AI (LLM API & Finetuning) 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="Gradient AI (LLM API & Finetuning) Analyst",
        goal="Access and analyze Gradient AI (LLM API & Finetuning) data via MCP.",
        backstory="Expert analyst with direct Gradient AI (LLM API & Finetuning) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Gradient AI (LLM API & Finetuning) 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 →

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Common questions about Gradient AI (LLM API & Finetuning) MCP in CrewAI

Pass the server URL in the `mcps` list of your Agent definition. The agents will automatically register the tools and gain the ability to call them.

Yes. CrewAI allows agents to share the context of tool outputs. If one agent creates a collection, others can use it immediately.

It does. You can assign specific tools to manager agents or subordinate agents, allowing for complex, multi-layered task execution.

Use the `tool_filter` option in the `MCPServerHTTP` class. This lets you restrict specific agents to only the tools they need for their role.

The server uses a zero-trust architecture. Every agent call is authenticated with your token, ensuring that only your crew can access your RAG collections and model weights.

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