How to Use the Pinecone MCP in CrewAI

Q: How do I add Pinecone tools to a CrewAI agent?

Pass the Vinkius MCP Server URL directly into your agent's mcps configuration array. CrewAI automatically registers the Pinecone toolset, allowing your specialized agents to invoke commands like queryvectors during their execution loops.

Q: Can multiple CrewAI agents query Pinecone simultaneously?

Yes, multiple agents can invoke queryvectors or fetchvectors concurrently within their tasks. CrewAI manages the execution state, ensuring that the research agent and the analyst agent can access the vector index without blocking each other's execution.

Q: Can CrewAI agents inspect Pinecone collections?

Yes, your database auditor agent can use listcollections to inspect available backups and metadata. The agent can then share this list with the rest of the crew to determine which collections are ready for archiving or deletion.

Deploy autonomous CrewAI agent teams to query, analyze, and maintain your Pinecone vector indexes without human intervention.

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Query Pinecone indexes using specialized CrewAI agents

The `query_vectors` tool allows your research agent to extract semantic matches via the MCP Server while a separate analyst agent evaluates the metadata. CrewAI coordinates this multi-agent workflow, letting one agent find relevant embeddings and another structure the final response. This separation of concerns prevents a single agent from becoming overloaded with context. Your agents share a memory space, meaning the results of a vector query are immediately accessible to the entire team. If the research agent pulls matching documents, the editorial agent can draft content based on those exact vectors. This teamwork makes complex semantic search pipelines run autonomously.

Audit Pinecone indexes with an autonomous CrewAI team

The `get_index_stats` tool uses the MCP protocol to enable your database monitor agent to keep track of active index capacities and vector counts. Working alongside a moderator agent, the team can analyze index performance and flag namespaces that require optimization. The entire process runs in the background without requiring developer intervention. If the monitor agent detects an unexpected spike in vector counts, it escalates the issue to the moderator agent. The moderator can then use `list_collections` to verify if temporary backups are taking up valuable index space. This automated oversight keeps your production vector store clean and efficient.

Coordinate index cleanups via an MCP Server in CrewAI

The `delete_vectors` tool gives your cleanup agent the ability to purge outdated records based on instructions from the supervisor agent. CrewAI's hierarchical execution pattern ensures that deletions only occur after the supervisor verifies the target IDs. This prevents rogue agents from executing bulk deletions without proper validation. Before executing a delete command, your supervisor agent can call `fetch_vectors` to double-check the record's metadata. Once verified, the cleanup agent executes the deletion, and the team logs the completed action to your shared memory. This gives you a structured, multi-agent safety net for database maintenance.

Setup guide

Set up Pinecone 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 Pinecone tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the Vinkius MCP Server URL directly into your agent's `mcps` configuration array. CrewAI automatically registers the Pinecone toolset, allowing your specialized agents to invoke commands like `query_vectors` during their execution loops.

Yes, multiple agents can invoke `query_vectors` or `fetch_vectors` concurrently within their tasks. CrewAI manages the execution state, ensuring that the research agent and the analyst agent can access the vector index without blocking each other's execution.

Your supervisor agent coordinates deletions by passing specific IDs to the cleanup agent using the `delete_vectors` tool. This multi-agent hierarchy ensures that deletions are verified against index metadata before any database records are permanently purged.

Yes, your database auditor agent can use `list_collections` to inspect available backups and metadata. The agent can then share this list with the rest of the crew to determine which collections are ready for archiving or deletion.

All vector queries and index metadata are processed locally within your crew's execution environment. The MCP Server acts as a stateless gateway, ensuring that your raw embeddings and database credentials are never stored or exposed outside your secure network tunnel.

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