How to Use the Vectara MCP in CrewAI

Q: How can I confirm available data sources for my crew using Vectara and CrewAI?

You run listcorpora to get all the corpus names. These keys are what you pass into your agents' tools, ensuring they only look at approved knowledge bases.

Q: What if an agent needs a summary of data with citations using Vectara and CrewAI?

The executeragchat tool handles this. The result is not just text; it's a grounded, summarized response that cites the original corpus documents, which builds trust in your autonomous operations.

Q: Can I see a list of all indexed files before running an operation with Vectara and CrewAI?

Yes, use listcorpusdocuments to check the exact contents of a corpus. This ensures that your agents aren't wasting time searching for documents that don't exist.

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Connect Vectara MCP to CrewAI

Create your Vinkius account to connect Vectara 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 Vectara with CrewAI

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Perplexity

Agent Specialization via MCP Server

CrewAI needs specialized knowledge. You expose the `perform_semantic_search` tool to allow one agent (the researcher) to pull context from multiple corpora keys. This keeps roles clean and focused. It means Agent A runs the search, gets the relevant data, and passes that specific output to Agent B for analysis.

Tracking Knowledge Bases

When building autonomous operations, knowing the scope of knowledge is critical. Use `list_corpora` to give your monitor agent a full directory of all available search datasets in Vectara. If an operation fails because it needs data from a non-existent source, you can't proceed until the moderator agent runs `get_corpus_details` on the correct corpus.

Corpus Data Maintenance

Autonomous operations sometimes require cleanup. The `delete_corpus_document` tool lets your team run irreversible data purging tasks, keeping the knowledge base clean and current. This is a high-risk action, so you'll want to wrap it in an explicit confirmation step within your CrewAI workflow.

Setup guide

Set up Vectara 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 Vectara tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Vectara transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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="Vectara Analyst",
        goal="Access and analyze Vectara data via MCP.",
        backstory="Expert analyst with direct Vectara access.",
        tools=mcp_tools,
    )

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

You run `list_corpora` to get all the corpus names. These keys are what you pass into your agents' tools, ensuring they only look at approved knowledge bases.

The `execute_rag_chat` tool handles this. The result is not just text; it's a grounded, summarized response that cites the original corpus documents, which builds trust in your autonomous operations.

The server touches indexed documents and metadata. The entire MCP Server interaction is sandboxed, meaning sensitive information remains contained during the agent execution cycle.

Yes, use `list_corpus_documents` to check the exact contents of a corpus. This ensures that your agents aren't wasting time searching for documents that don't exist.

The primary data types are indexed documents and associated metadata. The MCP Server handles both, keeping them isolated from the general environment.

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