How to Use the Vectara MCP in LangChain

Q: How do I check corpus status using Vectara with LangChain?

Use the getcorpusdetails tool. This action pulls metadata and configuration settings for a specific corpus you're interested in. It verifies that the dataset is set up correctly for agent consumption.

Q: What if I need to delete data using Vectara with LangChain?

You call deletecorpusdocument when an indexed document needs permanent removal from a corpus. Remember, this tool performs an irreversible deletion of the content you're targeting.

Q: Can I search across multiple datasets using Vectara with LangChain?

Yes. The performsemanticsearch tool allows you to pass comma-separated corpus keys, letting your agent query information from several different data sources at once.

Q: How do I list all available datasets using Vectara with LangChain?

Call the listcorpora tool. It gives you a complete inventory of every corpus (searchable dataset) housed within your connected Vectara account.

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Connect Vectara MCP to LangChain

Create your Vinkius account to connect Vectara to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Run RAG chats and get citations

The `execute_rag_chat` tool lets your agent query knowledge bases, returning an AI response that includes specific citations. This grounds the answer in source material, so you always know where the information came from. You can also use `perform_semantic_search` to find related concepts across multiple datasets before initiating a chat session. It's perfect for giving context to complex, multi-part queries.

Manage and list all data sources

You need to know what data is available first. The `list_corpora` tool gives you an inventory of every searchable dataset in the Vectara account. After that, `get_corpus_details` lets you check the specific configuration and metadata for any single corpus. Checking out your assets is simple. Use `list_corpus_documents` to list all files inside a specific corpus, or use `list_chat_sessions` to see history from previous agent interactions.

Delete documents irreversibly

Sometimes you gotta clean up data fast. The `delete_corpus_document` tool permanently removes an indexed document from a corpus. Be careful with this one, though—this action is irreversible. Because it's such a critical operation, the agent needs to confirm the target before running `delete_corpus_document`. This gives you full control over data lifecycle management within your LangChain pipeline.

Setup guide

Set up Vectara MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Vectara tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "vectara-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Vectara transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Vectara MCP in LangChain

Use the `get_corpus_details` tool. This action pulls metadata and configuration settings for a specific corpus you're interested in. It verifies that the dataset is set up correctly for agent consumption.

You call `delete_corpus_document` when an indexed document needs permanent removal from a corpus. Remember, this tool performs an irreversible deletion of the content you're targeting.

Yes. The `perform_semantic_search` tool allows you to pass comma-separated corpus keys, letting your agent query information from several different data sources at once.

Call the `list_corpora` tool. It gives you a complete inventory of every corpus (searchable dataset) housed within your connected Vectara account.

This server primarily handles Corpus Metadata, which includes the structural configuration and inventory details of your indexed datasets. The `get_corpus_details` tool is key to understanding these metadata types.

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