How to Use the Elasticsearch Vector MCP in CrewAI

Q: How do I give my CrewAI agents access to Elasticsearch Vector?

Pass the Vinkius server URL directly in the mcps list when defining your agent. This instantly exposes the tools to that specific agent via the MCP.

Q: Can multiple CrewAI agents use the same Elasticsearch Vector index simultaneously?

Yes, multiple agents can call the search tool concurrently. CrewAI manages the execution, allowing your research agent and your analysis agent to query the cluster at the same time.

Q: How does a CrewAI team handle schema mismatches in Elasticsearch Vector?

Your agent can call getindex to check the expected vector dimensions before running indexdocument. If there is a mismatch, the agent can log the error or route the task to a moderator agent.

Q: Is it possible to restrict certain CrewAI agents from deleting documents?

Yes, you can use the MCPServerHTTP class with a toolfilter to only expose the search tool to your research agents, while reserving deletedocument for your admin agent.

Let your CrewAI agent teams collaborate on complex search tasks directly inside your Elasticsearch Vector index.

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Connect Elasticsearch Vector MCP to CrewAI

Create your Vinkius account to connect Elasticsearch Vector 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 Elasticsearch Vector with CrewAI

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Multi-agent collaborative vector search

Invoking the `search` tool allows your specialized CrewAI agents to query your cluster for highly relevant context. One agent can generate the query embedding, while a second agent runs the search to retrieve the top kNN matches. This collaborative setup ensures that your agents aren't just searching blindly. They share the retrieved document context through their shared MCP memory, allowing the entire crew to make decisions based on the same vector data.

Autonomous index management via CrewAI MCP Server

Deploying the `create_index` tool gives your moderator agent the ability to configure new vector spaces when a new project begins. The agent uses `get_index` to inspect existing mappings and ensure they match the required dimensions. If an index becomes cluttered or obsolete, the crew can coordinate to clean it up. They use `list_indexes` to audit the cluster and keep your hosting costs down by identifying unused resources.

Coordinated document indexing and pruning

Writing data via the `index_document` tool is used by your writing agents to save new knowledge pieces into the vector store. This happens autonomously as your crew researches topics and compiles reports. When information becomes outdated, a monitoring agent triggers the `delete_document` tool. This keeps your vector search accurate and prevents older, irrelevant documents from throwing off future kNN queries.

Setup guide

Set up Elasticsearch Vector 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 Elasticsearch Vector tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the Vinkius server URL directly in the `mcps` list when defining your agent. This instantly exposes the tools to that specific agent via the MCP.

Yes, multiple agents can call the `search` tool concurrently. CrewAI manages the execution, allowing your research agent and your analysis agent to query the cluster at the same time.

Your agent can call `get_index` to check the expected vector dimensions before running `index_document`. If there is a mismatch, the agent can log the error or route the task to a moderator agent.

Yes, you can use the `MCPServerHTTP` class with a `tool_filter` to only expose the `search` tool to your research agents, while reserving `delete_document` for your admin agent.

We enforce strict transport encryption on this MCP Server and run the server in an ephemeral sandbox. Your dense vectors, embedding documents, and index mappings are processed in-memory and sent directly to Elasticsearch, leaving zero trace on Vinkius.

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