How to Use the pgvector (Vector Database) MCP in CrewAI

Q: Can a CrewAI agent dynamically partition data in pgvector (Vector Database)?

Yes, it can. An agent can call createtable to spin up a new vector table for a specific task, ensuring that different agents do not overwrite or access each other's vector datasets.

Q: How does CrewAI handle database indexing with pgvector (Vector Database)?

You assign a maintenance agent to run createindex after high-volume write operations. This keeps search speeds fast for the rest of your CrewAI squad without interrupting their execution flow.

Q: What happens if an agent inserts a corrupted vector?

You use the deletevector tool to remove the invalid record. A supervisor agent can monitor search quality and trigger deletions when it detects anomalous vector dimensions.

Let specialized agent teams query, organize, and prune your pgvector database autonomously using CrewAI.

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Setup pgvector (Vector Database) with CrewAI

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Collaborative database searches with CrewAI

`search_vectors` allows a research agent inside your CrewAI squad to locate relevant context before handing tasks off to a writer agent. Instead of querying the database blindly, one agent performs semantic lookups while another filters the metadata. This division of labor reduces token usage and prevents agents from hallucinating outdated information. By exposing this MCP Server to the entire crew, your agents share a common memory layer backed by PostgreSQL. The entire operation executes autonomously, pulling exact database matches without human prompts.

Autonomous schema management

`create_table` is used by your database administrator agent when it detects a new project or tenant requires isolated storage. The agent checks existing schemas using `list_tables` and creates a matching table with the correct vector dimensions. This automated partitioning keeps your database organized without manual DBA intervention. Running this through our managed server ensures that the crew cannot execute arbitrary SQL injections. The agent is strictly limited to the structured parameters defined by the MCP tool.

Direct vector insertion from multi-agent pipelines

`insert_vector` lets your ingestion agents save processed documents directly into PostgreSQL as vector embeddings. As one agent parses a PDF, another generates the embedding and inserts it into the database. The pipeline runs continuously, updating your search index in real-time. This direct write capability eliminates the need for separate ETL pipelines. Your CrewAI agents handle both the data processing and the database insertion in a single execution flow.

Setup guide

Set up pgvector (Vector Database) 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 pgvector (Vector Database) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent pgvector (Vector Database) 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 pgvector. 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 pgvector (Vector Database) MCP in CrewAI

Your CrewAI agents connect to the MCP Server and use a shared memory pool to pass table names and query results. While one agent runs `search_vectors` to extract coordinates, a moderator agent uses that data to execute downstream tasks.

Yes, it can. An agent can call `create_table` to spin up a new vector table for a specific task, ensuring that different agents do not overwrite or access each other's vector datasets.

You assign a maintenance agent to run `create_index` after high-volume write operations. This keeps search speeds fast for the rest of your CrewAI squad without interrupting their execution flow.

You use the `delete_vector` tool to remove the invalid record. A supervisor agent can monitor search quality and trigger deletions when it detects anomalous vector dimensions.

Yes, all data operations are strictly sandboxed by the MCP Server. The server only exposes specific tools like `search_vectors` and `insert_vector`, meaning your autonomous CrewAI agents can never execute raw, destructive SQL commands on your database records.

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