How to Use the Typesense Vector Search MCP in CrewAI

Q: How does CrewAI use Typesense Vector Search?

Agents call searchvectors, passing the collection name, query text, and vector string. The results are automatically incorporated into the crew's shared memory for subsequent analysis.

Q: Is it possible to manage collections using Typesense Vector Search with CrewAI?

Yes. You can createcollection a new schema, or use getcollectiondetails to check the current metadata and structure of any existing resource.

Q: What is the best way to start using the full tool set with CrewAI?

You can call listvectorcollections first to see all available resources, then proceed with defining schemas or running searches using the other five tools.

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Create your Vinkius account to connect Typesense Vector Search 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 Typesense Vector Search with CrewAI

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Execute Vector Search in the CrewAI Team

A specialized agent runs `search_vectors` when it needs external context. You give it the collection name, a text query, and the vector string. The result feeds back into the crew's shared memory. This lets your autonomous operations use up-to-date semantic knowledge for informed decision-making without human intervention.

Initialize Data Sets with CrewAI

When setting up a new operation, an agent calls `create_collection` to define the required schema using a JSON object. This ensures data consistency across all team members. The crew can then use `list_vector_collections` to ensure they are pointing at the correct resource for their task.

Manage Search Data via MCP Server

To update existing records, an agent uses `index_document`, passing in the collection name and the JSON payload. The server handles merging or replacing the old data. Need to clean up? `delete_document` permanently removes a record by its ID. This is best used when the data is confirmed irrelevant.

Setup guide

Set up Typesense Vector Search 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 Typesense Vector Search tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Agents call `search_vectors`, passing the collection name, query text, and vector string. The results are automatically incorporated into the crew's shared memory for subsequent analysis.

Yes. You can `create_collection` a new schema, or use `get_collection_details` to check the current metadata and structure of any existing resource.

The tool removes records by ID. This affects document content within a specific collection, making that record permanently unavailable for search or indexing.

This server touches search collection schemas and document content. All types of vector data are managed by Typesense Vector Search.

You can call `list_vector_collections` first to see all available resources, then proceed with defining schemas or running searches using the other five tools.

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