Vectara MCP. Ground answers using internal knowledge bases.
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Vectara connects your agent to secure, internal data sources for Retrieval-Augmented Generation (RAG). It lets you query private documents using natural language, run interactive chats against specific knowledge bases (corpora), and manage the document indexes themselves.
This is how you turn a pile of PDFs into an active, searchable database.
What your AI agents can do
Delete corpus document
Permanently removes an indexed document from a specific knowledge corpus. This action cannot be undone.
Execute rag chat
Runs an interactive, chat-style session that provides AI answers and cites the sources based on provided corpora keys.
Get corpus details
Retrieves all metadata and configuration information for a single specified corpus key.
You pass the agent one or more corpus keys and a query; it then executes a semantic search across all specified datasets, returning highly relevant document snippets.
The agent runs an interactive, RAG-powered chat session on specific corpora, providing detailed answers with direct citations to the source documents.
It retrieves a list of every searchable dataset (corpora) configured in your Vectara account so you know what data is available for querying.
The agent pulls configuration details, unique keys, or full lists of documents within any given corpus to help you audit the data structure.
You permanently delete specific documents from a corpus using their key. This action is irreversible and keeps your search results clean.
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Vectara: 7 Tools for Enterprise Data Management
Use these seven tools to list, query, delete, and audit your private document corpora using the power of an AI agent.
019d761bdelete corpus document
Permanently removes an indexed document from a specific knowledge corpus. This action cannot be undone.
019d761bexecute rag chat
Runs an interactive, chat-style session that provides AI answers and cites the sources based on provided corpora keys.
019d761bget corpus details
Retrieves all metadata and configuration information for a single specified corpus key.
019d761blist chat sessions
Returns a list of IDs for previous RAG chat sessions that have been completed.
019d761blist corpora
Lists every searchable dataset (corpus) configured within your entire Vectara account.
019d761blist corpus documents
Retrieves a list of all individual indexed documents contained inside one specific corpus key.
019d761bperform semantic search
Executes a semantic search query across multiple specified corpora, finding the most relevant document snippets based on your input text.
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What you can do with this MCP connector
You gotta connect your agent to the good stuff—the private data locked up in internal wikis and runbooks. This server does that, giving your AI client Retrieval-Augmented Generation (RAG) right where you need it. It turns a messy pile of PDFs into an active, searchable database.
When you're querying private documents semantically, the agent uses perform_semantic_search. You pass in one or more corpus keys and your query; it executes a search across all specified datasets, pulling back highly relevant document snippets. It doesn't just look for keywords—it finds the context that matters.
Want an actual conversation? Use execute_rag_chat. This runs a full, chat-style session on specific corpora. Your AI client gives you detailed answers and cites exactly which source documents it pulled from to back up every single claim. You'll also get a list of IDs for previous RAG chat sessions using list_chat_sessions, so you can keep track of what was talked about.
Managing your knowledge base is just as important. To see everything available, run list_corpora; it spits out every searchable dataset (corpus) configured in your whole Vectara account. If you need to drill down into a specific data source, get_corpus_details pulls all the metadata and configuration info for that single corpus key.
Need to know what's actually inside? You can use list_corpus_documents, which returns a full list of every individual document indexed within one specified corpus key. And if you find stale or obsolete files, don't sweat it—delete_corpus_document lets you permanently remove specific documents from a corpus using their key. Just remember, that action is irreversible.
This server handles the whole cycle: running deep semantic searches, having cited chats, listing all available knowledge sources, and maintaining the integrity of your document indexes.
How Vectara MCP Works
- 1 First, subscribe to the server and enter your Vectara API Key and Customer ID.
- 2 Next, point your AI agent at a specific corpus key or ask it to list all available corpora using
list_corpora. - 3 Finally, use the agent to either run a targeted semantic query (
perform_semantic_search) or start an interactive chat session (execute_rag_chat).
The bottom line is that your AI client gets direct access to your internal knowledge graphs, making it act like a specialized data librarian for your company.
Who Is Vectara MCP For?
This server is for technical teams dealing with massive amounts of unstructured knowledge. Think software engineers debugging complex systems or product leads who need answers from outdated manuals without waiting on the front-end team. If your work relies on knowing exactly what a document says, this is for you.
Runs direct query tests via chat to debug RAG implementation challenges instead of writing disposable test scripts.
Uses commands like delete_corpus_document or listing tools to manually audit and remove stale context arrays from the knowledge base.
Asks complex questions against internal product manuals stored in a corpus without waiting for formal documentation updates or UI development.
What Changes When You Connect
- Precision over Keywords: Instead of relying on exact keyword matches,
perform_semantic_searchfinds documents that mean what you're asking. You query 'how do I reset the API key,' and it pulls the right section even if those words aren't in the document title. - Contextual Chat Flow: With
execute_rag_chat, your AI agent maintains conversation context across multiple turns. It doesn't forget what you asked three prompts ago, giving you a true back-and-forth dialogue with your documentation. - Auditing and Cleanup: The admin tools are critical. Use
list_corpus_documentsto see exactly which files make up a corpus, and usedelete_corpus_documentwhen stale info pollutes the search results. - Cross-Corpus Discovery: You don't have to manually check every manual. By listing multiple corpora with
perform_semantic_search, your agent can draw knowledge from HR guides and DevOps runbooks in one go. - History Tracking: Need to reference a past conversation?
list_chat_sessionsgives you the IDs for old RAG chats, letting you pick up exactly where you left off without starting over.
Real-World Use Cases
Debugging an unknown Kubernetes failure
A DevOps engineer hits a critical bug in production. Instead of digging through Slack threads and old tickets, they ask their agent: 'What was the rollback procedure for v2.1.0?' The agent runs execute_rag_chat across the 'DevOps Runbooks' corpus and immediately returns the citation from the correct incident report.
Drafting a technical product guide
A Technical Writer needs to write a section on user permissions. They use their agent to run perform_semantic_search against three different corpora: 'HR Manuals', 'Security Policies', and 'Product Spec V3'. The agent gathers all required details, ensuring the new document is accurate across all departments.
Cleaning up obsolete internal data
A Data Engineer knows a project was decommissioned last month. They use list_corpus_documents to find every file related to that project and then run delete_corpus_document on each one, keeping the knowledge base clean.
Finding niche answers across departments
A Product Lead asks: 'What are the requirements for integrating a third-party API?' The agent runs a semantic search across all relevant corpora—'Product Specs', 'Legal Docs', and 'Integration Guides'—and synthesizes one answer with citations from three different sources.
The Tradeoffs
Treating the knowledge base like Google.
Just pasting a vague query into an unstructured chat without specifying which corpora to search. The agent might pull random, unverified data from unrelated sources.
→
Always start by using list_corpora to confirm your dataset keys. Then, specify the relevant keys (e.g., 'Search corpus A and B') when running perform_semantic_search or execute_rag_chat.
Relying only on chat for data validation.
Assuming that because an answer was generated, it must be correct. The agent might hallucinate or misinterpret the source material.
→
Use get_corpus_details and list_corpus_documents first to validate the corpus structure and metadata. If you need certainty, run a highly specific perform_semantic_search query.
Ignoring document lifecycle management.
Letting outdated manuals or decommissioned procedures remain indexed in the search space, leading to incorrect answers months later.
→
Periodically audit your data. Use list_corpus_documents to see what's inside a corpus, then use delete_corpus_document to permanently remove stale content.
When It Fits, When It Doesn't
Use this server if you need your AI agent to answer questions based on your private documents. It’s about grounding the response in facts that only exist inside your company walls. If you're building a chatbot for public FAQs, other tools might work better. But if the data is proprietary—like internal runbooks or HR policies—you need Vectara.
Don't use this just because you want to 'streamline.' Use it when you require precision. If your task is simply summarizing general concepts that are already widely known, a standard LLM prompt works fine. But if the question is, 'What did Bob write in the Q3 meeting notes about the API change?'—you need Vectara's combination of list_corpora for scope and execute_rag_chat for context.
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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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Finding a specific policy detail shouldn't require jumping between three different internal sites.
Today, finding the answer to a complex question means opening Jira, pulling up Confluence, and then cross-referencing a shared drive. You spend 20 minutes copying text from one page into another just to piece together the full picture. It’s slow, and you always miss something.
With this MCP server, your agent handles that messy process for you. By running tools like `perform_semantic_search`, it checks all relevant indexed corpora in seconds. You get a single answer with citations pointing directly back to the three different documents it used.
Vectara MCP Server: Grounding Chat Context
Manual chat agents often lose context after a few turns, forcing you to restate background details and making follow-up questions clunky. You’re stuck in simple Q&A cycles that don't feel like having a real conversation.
The `execute_rag_chat` tool changes the game. It maintains conversational history while grounding every single response in your corpus data. The agent remembers what you said ten minutes ago and uses it to answer your current question—it works like talking to an expert who already read all your company's manuals.
Common Questions About Vectara MCP
How do I find out which documents are available for search using list_corpora? +
Run list_corpora first. This command shows you every dataset (corpus) currently indexed in your account. It's the necessary starting step to know what knowledge is even available.
Is perform_semantic_search better than execute_rag_chat for finding data? +
It depends on the goal. Use perform_semantic_search when you just need a quick dump of the top 3 matching documents (the evidence). Use execute_rag_chat when you want a synthesized, narrative answer built from those sources.
What if I find bad data in my corpus? Can I remove it using delete_corpus_document? +
Yes. If you locate an obsolete or incorrect file, use delete_corpus_document with the specific document key. Remember this action is irreversible.
Can I see what corpora are available for my team to search? +
Run list_corpora. This command gives you a full, structured list of all your company’s knowledge bases and their unique keys.
What critical configuration data can I check using the `get_corpus_details` tool? +
The tool provides metadata and current settings for a specific corpus. You'll see details like the corpus creation date, its owner ID, and whether it has any active indexing limits applied. This confirms your dataset is properly set up before running queries.
If I use `list_corpus_documents`, what information do I get about each indexed file? +
It lists every document inside a corpus, giving you the unique internal key and the file's last modified date. This helps inventory control and ensures you know exactly which files are contributing to search results.
When I run `perform_semantic_search` with an invalid or non-existent corpus key, what error should I expect? +
The agent will return a specific API error indicating that the provided corpus keys do not resolve to any active dataset. This means you need to verify your input keys against the output of list_corpora.
How can I use `list_chat_sessions` to audit my previous RAG conversations? +
This tool retrieves a list of past chat sessions, providing the unique session ID and when it was initiated. It's perfect for quickly finding context histories you need to review or reference later.
Can I query my internal documents directly using just conversational chat? +
Yes. If your data is indexed in a Vectara corpus, simply ask your agent: search the 'employee-handbook' corpus for remote work policies. The agent uses the queryTool to pass your question to Vectara's semantic engine, effortlessly bringing back precisely matching paragraph citations instantly.
How do I remove outdated context files destroying the accuracy of my RAG model? +
You don't need to rebuild APIs or use cURL. Tell your AI: delete document ID 'doc-992a' from my Sales corpus. It automatically formats the mutation and wipes the poisoned embedding from Vectara's nodes permanently, restoring high accuracy.
Will the RAG Chat tool provide accurate source citations? +
Yes. When you instruct the agent to run execute_rag_chat, Vectara processes the query against its internal LLM and index, returning a synthesized natural language answer appended solidly with exact document citations, proving the AI isn't hallucinating facts.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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