How to Use the Voyage AI (AI Embeddings API) MCP in CrewAI

Q: How does Voyage AI (AI Embeddings API) support autonomous operations in CrewAI?

The MCP Server allows your agents to call tools like createembeddings and then use the results immediately. This seamless connection enables true autonomy, letting the crew work without a human telling it what to do next.

Q: Can Voyage AI (AI Embeddings API) handle multimodal content in CrewAI?

Yes. The createmultimodalembeddings tool lets the MCP Server generate vectors from images and text, giving your specialized agents a much richer pool of data to analyze.

Q: How do I use Voyage AI (AI Embeddings API) for initial research in CrewAI?

You start by using createembeddings or createcontextualizedembeddings. The resulting vectors are the shared memory that Agent A uses to conduct its deep, informed research.

Q: Does Voyage AI (AI Embeddings API) support deleting files in CrewAI?

It does. You can use deletefile after a batch job is complete or if the data is no longer needed by the crew. This keeps your workspace clean and efficient.

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Setup Voyage AI (AI Embeddings API) with CrewAI

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Specialized Embeddings for CrewAI Agents

Agent A needs context, and that’s where `create_contextualized_embeddings` comes in. This tool builds embeddings based on the surrounding text chunk, allowing your agent to perform deep analysis. When Agent B analyzes a document using these specific vectors, it's far more accurate than simple keyword matching—it understands the *role* of the data.

Managing Large Data Sets with MCP Server

Don't process files one by one. Use `create_batch` to send massive amounts of documents for embedding jobs. The CrewAI system can then trigger checks using `get_batch` when the results are ready. The MCP Server handles all the orchestration, allowing your multi-agent crew to proceed autonomously without manual intervention.

Advanced RAG with Voyage AI (AI Embeddings API)

For a critical step in any autonomous workflow, use the `rerank` tool. This function compares documents against a query and scores them by relevance. It's a crucial quality gate for your agents. The CrewAI monitor agent can call this tool to validate that the research phase completed successfully before allowing the analysis agent to proceed.

Setup guide

Set up Voyage AI (AI Embeddings API) 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 Voyage AI (AI Embeddings API) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Voyage AI (AI Embeddings API) 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 Voyage AI. 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 Voyage AI (AI Embeddings API) MCP in CrewAI

The MCP Server allows your agents to call tools like `create_embeddings` and then use the results immediately. This seamless connection enables true autonomy, letting the crew work without a human telling it what to do next.

This server touches files and embeddings. Because your agents are running complex, multi-step operations, you must manage which source documents are passed to `upload_file` for processing.

Yes. The `create_multimodal_embeddings` tool lets the MCP Server generate vectors from images and text, giving your specialized agents a much richer pool of data to analyze.

You start by using `create_embeddings` or `create_contextualized_embeddings`. The resulting vectors are the shared memory that Agent A uses to conduct its deep, informed research.

It does. You can use `delete_file` after a batch job is complete or if the data is no longer needed by the crew. This keeps your workspace clean and efficient.

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