Glean MCP for AI. Search every document and person in your company.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Glean connects your AI agent to all your company's data—Confluence, Slack, Google Drive, Jira, and more. It lets you ask natural questions about any internal document or person, getting instant answers without clicking through dozens of siloed apps.
What your AI can do
Autocomplete
Provides intelligent search suggestions based on known company terminology as you type.
Bulk index documents
Indexes a large number of documents to make them searchable at once.
Chat
Asks natural language questions and gets AI-generated answers grounded in your internal knowledge base.
Search content simultaneously across dozens of connected apps like Confluence, Slack, and Jira.
Ask natural language questions to generate answers that are directly cited from your company's own documentation.
Find employee details, including their role, expertise, and department, across the entire organization.
Focus your search results only on a single platform, like pulling everything from just Google Drive.
Programmatically index new documents or manage curated knowledge collections for IT teams.
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Glean: 12 Tools for Enterprise Search
These tools give your agent precise control over searching, indexing, and managing content across every connected system.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Glean on VinkiusAutocomplete
Provides intelligent search suggestions based on known company terminology as you type.
Bulk Index Documents
Indexes a large number of documents to make them searchable at once.
Chat
Asks natural language questions and gets AI-generated answers grounded in your...
Check Glean Status
Verifies the current connectivity status of the entire MCP to ensure data is flowing...
Delete Document
Permanently removes a specific document from your connected knowledge sources.
Get Collection
Retrieves detailed information about a specific, curated set of related documents.
Get Document
Gets the metadata and details for one single document file based on its ID.
Index Document
Adds a single, new document to your searchable knowledge repository.
List Collections
Shows you all the pre-built and curated groups of documents available in your system.
Search By Datasource
Narrows a general search to look only within one specific connected source, like...
Search People
Finds employee records by name, department, role, or area of expertise across the...
Search
Performs a broad search query that scans content across every connected platform simultaneously.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Glean, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Glean. 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 connection provides 12 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Finding internal answers feels like a scavenger hunt across half a dozen applications.
Right now, if you need to know the status of a project, you might check Jira for tickets, then open Confluence for technical specs, and finally message Slack to see who's on call. You spend ten minutes just opening tabs, copy-pasting details between them, and hoping one piece of information isn't outdated.
With this MCP, that process disappears. Instead of clicking through apps, you simply talk to your agent. It pulls the project status from Jira, checks the specs in Confluence, and tells you who’s on call via Slack—all without you leaving your chat window. You get one single answer.
Use search across all content.
You no longer have to remember which tool holds the right information. The dedicated `search` function handles the complexity of querying multiple platforms, and you only deal with the answer. You can also use `autocomplete` as you type a query to guide your search instantly.
The difference is simple: instead of managing data source connections and permissions in your head, this MCP does it for you. It turns scattered knowledge into one unified conversation.
What your AI can actually do with this
Imagine needing an answer that lives somewhere between the engineering wiki, a Slack thread from three months ago, and a payroll sheet in Google Drive. Usually, you'd spend 20 minutes opening five different tabs just to piece together the context. This MCP lets your AI client bypass all that friction.
Instead of searching tool by tool, you ask one question—like 'Who owns the API documentation for the payments team?'—and get a single answer compiled from every connected source. You don't manage data sources; you just talk to them. By connecting Glean through Vinkius, your agent instantly gains visibility across your entire enterprise knowledge base, turning fragmented documents and chat logs into actionable intelligence.
019dd0fb-24fd-7369-9ba8-cc1c216b2cbb Here's how it actually works
The bottom line is that you treat your entire company's knowledge like one giant, searchable file cabinet.
Subscribe to this MCP and provide your Glean API Token in the Vinkius admin console.
Your agent uses the connection to pull data from all connected enterprise sources.
You ask a question or execute a search query through your AI client, receiving consolidated results.
Who is this actually for?
Knowledge workers who spend half their day searching for internal documents and answers. Anyone frustrated by the constant need to jump between five or six different enterprise applications just to complete a single task.
Needs to find specific policies, deprecated API usage notes, or old design decisions buried in Confluence and Slack threads.
Must programmatically manage knowledge bases by using tools like index_document or listing available sets of curated content via list_collections.
Needs to quickly find out who built a feature, where the requirements are documented, and what current discussions exist about it across multiple platforms.
What Changes When You Connect
Find answers instantly. Instead of manually opening Jira, Confluence, and Drive to answer a question, using the chat tool lets you ask your agent once and get all relevant results cited from multiple sources.
Stop wasting time jumping between tabs. The general search function reads across every connected app—people records, wikis, chats—making discovery feel like one unified database.
Target specific knowledge sets. If you know the answer is only in Slack threads but don't want to wade through Jira tickets, use search_by_datasource to limit your query scope.
Manage content programmatically. For IT teams, functions like index_document and bulk_index_documents let you update knowledge sources without manual intervention.
Discover people faster than HR can find them. The dedicated search_people tool lets you look up expertise or department leads instantly, solving that 'who do I ask?' problem.
See it in action
Troubleshooting a legacy API endpoint
A developer needs to know the original owner and implementation notes for an old payment microservice. Instead of guessing which department owns the documentation, they use search_people to find the former lead engineer, then run a general search query focused on that person's name in Confluence.
Preparing for an internal audit
The compliance officer needs proof of all current security policies. They use list_collections to find the official 'Security Policy' collection, then run a specific search on that collection using get_collection details to compile the necessary reports.
Onboarding a new hire
A new team member can ask the AI agent: 'What are our current vacation policies?' The chat function responds with policy documents found across HR Drive and Confluence, all in one summarized response.
The honest tradeoffs
Searching for everything generically
The user types 'payroll' into the main search bar but gets results from marketing blogs mixed with HR documents. They waste time sifting through irrelevant articles.
If you only care about official policies, use search_by_datasource and select 'Confluence'. If you need to find people involved in payroll, use search_people first.
Trying to manage data manually
An admin wants to make 50 old meeting notes searchable, so they copy-paste them into a single Google Doc and forget about it.
Instead of manual cleanup, use the bulk_index_documents tool. This indexes large batches of files directly into your search index.
Asking for data that doesn't exist
A user asks the chat function: 'What is our Q3 revenue target?' The system correctly responds it can't find this because financial projections are in a locked, unconnected spreadsheet.
Always check connectivity first. Use check_glean_status to confirm all necessary data sources are online and connected before asking for critical business metrics.
When It Fits, When It Doesn't
Use this MCP if your core problem is locating information—finding the right document, person, or policy buried in disparate company apps. You need context retrieval. Don't use it if you need to perform an action that changes data state (like sending emails or updating CRM records); for those, look at dedicated integration tools. If all you need is a simple list of files without searching their content, stick to the native file explorer. Use get_document when you know exactly which document ID you want details on, but use general search when you just have an idea of what you're looking for.
Questions you might have
How do I check if the Glean MCP is working correctly? +
Run the check_glean_status tool. This instantly verifies connectivity and confirms that all your connected data sources are available for searching.
Can I use search_by_datasource to limit my query? +
Yes. Using search_by_datasource, you tell the MCP to look only within a single platform, like Confluence or Google Drive, which is useful when you know where the answer must live.
How do I find an employee's expertise? +
Use search_people. You can search by name, role, or specific area of expertise to locate who knows what within your organization.
What is the difference between general search and chat? +
General search returns multiple snippets of text from where it found information. The chat tool uses AI to synthesize those results and give you a single, summarized answer.
When I use `bulk_index_documents`, how does that affect my existing knowledge base? +
It processes and adds new content without disrupting live searches. You can upload large batches of documents to immediately expand your searchable data pool.
If I run `list_collections`, what kind of information do I receive about my content groups? +
The tool returns a list of all curated collections associated with your workspace. From this list, you can select specific groupings to view their contents and policies.
What is the process when I use the `delete_document` tool? +
This action permanently removes a specified document from the index and search results. Use it carefully, as this deletion cannot be undone once executed.
How does running `autocomplete` help me find information faster? +
It provides intelligent suggestions based on the full scope of your connected knowledge. As you type, it anticipates relevant terms and people mentioned across all sources.
Can I search across multiple data sources like Confluence, Slack, and Google Drive simultaneously? +
Yes! The search tool queries all connected data sources in a single request. If you want to narrow results to a specific platform, use search_by_datasource with the datasource name (e.g., 'Confluence', 'Slack', 'Google Drive'). Use autocomplete to get intelligent suggestions as you refine your query.
Can I ask the Glean AI assistant questions about my organization's knowledge? +
Yes. The chat tool sends a natural language question to Glean's AI assistant, which generates an answer grounded in your organization's indexed knowledge — including documents, wikis, Slack conversations, and Jira tickets. This is ideal for getting synthesized answers rather than just search results.
Can I index custom documents into Glean programmatically? +
Yes. Use index_document to add a single document with a JSON payload containing title, body, URL, and metadata. For large batches, use bulk_index_documents to index multiple documents in one request. Use delete_document to remove outdated content and get_document to verify indexing status.
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