How to Use the Vectara MCP in OpenAI Agents SDK
Build production agents using your AI client: OpenAI Agents SDK.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Vectara MCP to OpenAI Agents SDK
Create your Vinkius account to connect Vectara to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Grounding agent responses with RAG
When the agent needs answers, it executes a RAG-powered chat completion via `execute_rag_chat`. This gives summarized AI responses and citations directly from your indexed data. It handles complex knowledge retrieval by running `perform_semantic_search` across multiple corpora. The system doesn't guess; it cites the source materials for every answer.
Managing and listing datasets
You can manage your entire data estate using these tools. Call `list_corpora` to see all search-ready datasets in the account, or use `list_corpus_documents` for a specific dataset. Need to know what's inside? Run `get_corpus_details` to pull metadata and configuration for any given corpus.
Maintaining data integrity
If you find stale or incorrect information, the agent can permanently remove it using `delete_corpus_document`. Remember, this action is irreversible, so check your work first. It also lets you review history. Call `list_chat_sessions` to see a record of previous RAG chat activity for debugging or auditing.
Set up Vectara MCP in OpenAI Agents SDK
Prerequisites
- Python 3.10+ installed
-
openai-agentspackage (pip install openai-agents) - Active Vinkius subscription with a valid endpoint token
- 1
Install the SDK
Run
pip install openai-agentsto install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed. - 2
Connect via SSE transport
Use
MCPServerSsewith your Vinkius endpoint URL. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. The SDK auto-discovers all Vectara tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives Vectara tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate Vectara tools and returns structured results. Copy the full example on the right to get started.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse
async def main():
async with MCPServerSse(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as server:
agent = Agent(
name="Vectara Agent",
instructions="You have access to Vectara tools.",
mcp_servers=[server],
)
result = await Runner.run(agent, "List recent transactions")
print(result.final_output)
asyncio.run(main())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.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about Vectara MCP in OpenAI Agents SDK
Use it with your favorite AI tools
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