Vectara MCP Server for AutoGen 7 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Vectara as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="vectara_agent",
tools=tools,
system_message=(
"You help users with Vectara. "
"7 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Vectara MCP Server
Connect your Vectara environment to any AI agent to unlock enterprise-grade Retrieval-Augmented Generation (RAG) and semantic search directly inside your conversational IDE or workspace.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Vectara tools. Connect 7 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- Semantic Search — Query your indexed private corpora naturally and return highly relevant, grounded documents without traditional keyword matching limitations.
- Conversational RAG — Execute fully-fledged interactive chats leveraging Vectara's backend to provide detailed, cited answers strictly based on your secure documents.
- Corpus Management — List all available data corpora, retrieve unique keys, and discover the shape of your indexed data environment on the fly.
- Document Auditing — Monitor specific document indexes within a corpus, verify correct ingestions, or permanently delete obsolete files avoiding polluted search results.
The Vectara MCP Server exposes 7 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Vectara to AutoGen via MCP
Follow these steps to integrate the Vectara MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 7 tools from Vectara automatically
Why Use AutoGen with the Vectara MCP Server
AutoGen provides unique advantages when paired with Vectara through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Vectara tools to solve complex tasks
Role-based architecture lets you assign Vectara tool access to specific agents — a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Vectara tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Vectara tool responses in an isolated environment
Vectara + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Vectara MCP Server delivers measurable value.
Collaborative analysis: one agent queries Vectara while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Vectara, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Vectara data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Vectara responses in a sandboxed execution environment
Vectara MCP Tools for AutoGen (7)
These 7 tools become available when you connect Vectara to AutoGen via MCP:
delete_corpus_document
This action is irreversible. Permanently removes a document from a corpus
execute_rag_chat
Provide corpus keys and the user query to get a summarized AI response with citations. Executes a RAG-powered chat completion
get_corpus_details
Retrieves metadata and configuration for a specific corpus
list_chat_sessions
Lists previous RAG chat sessions
list_corpora
Lists all corpora (searchable datasets) in the Vectara account
list_corpus_documents
Lists all indexed documents within a specific corpus
perform_semantic_search
Provide one or more comma-separated corpus keys and the query text. Executes a semantic search across one or more corpora
Example Prompts for Vectara in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with Vectara immediately.
"List all configured knowledge corpora I have in Vectara."
"Query corpus `cor-81a` for instructions on 'rolling back kubernetes pods' and show only the top 3 best matching results."
"List all active chat context session IDs for the last week."
Troubleshooting Vectara MCP Server with AutoGen
Common issues when connecting Vectara to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Vectara + AutoGen FAQ
Common questions about integrating Vectara MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Vectara with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Vectara to AutoGen
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.