Glean MCP. Search Every SaaS App, Not Just One.
Glean MCP searches your entire company's knowledge base, connecting conversations across every SaaS app—Jira, Confluence, Slack, etc. Instead of digging through dozens of tabs and siloed databases, you ask your agent a question once and get AI-generated answers synthesized from corporate data instantly.
Give Claude and any AI agent real-world access
The MCP searches all connected SaaS applications to retrieve and synthesize answers based on company documents, chat logs, and project records.
It looks up active directory information, matching user names and skills across the organization so you can find who knows what.
The MCP maintains historical context for complex chats, allowing your agent to handle multi-step reasoning over time without losing track of the thread.
You can upload custom documentation or index existing files directly into the corporate search system.
It provides mechanisms to permanently delete indexed documents, ensuring sensitive data is removed from future searches and retrievals.
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What AI agents can do with Glean: 10 Tools for Enterprise Search
These tools let you search documents, identify people, manage conversation history, and index data across your entire company's ecosystem.
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 MCPGet Answer
Retrieves specific billing information by running structured rules against active account data.
Autocomplete
Predicts potential page destinations using partial text inputs, helping you navigate...
Chat Completion
Manages ongoing text conversations while keeping track of the entire chat history...
Custom Request
Executes specific, custom data requests across different internal systems using...
Delete Document
Removes indexed documents permanently from the search index and blocks future...
Search Datasource
Performs structural data extraction to query properties across different connected accounts or systems.
Index Document
Adds new documents, articles, or records into the corporate knowledge base for AI search access.
Search People
Generates a structured JSON payload that identifies and retrieves contact details...
Search Docs
Searches for bounded records inside the platform, finding documents across all...
Get Suggestions
Extracts rich data flags and suggestions by analyzing validation rules within your...
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 each 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 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
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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Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
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EU data residency
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~60% cost reduction
The Knowledge Silo Problem
Today, finding a simple answer requires an exhausting detective process. You start in Slack to see who discussed a topic, then you jump to Confluence because that's where the official policy should be. If that fails, you check Jira for linked tickets and finally, you might have to search Google Drive just to find the original presentation slide. This manual clicking and copy-pasting is slow, frustrating, and rarely complete.
With this MCP, you talk to your agent like talking to a coworker who already knows everything. You ask: 'What was our policy on Q3 travel?' And instantly, it pulls data from finance documents, HR handbooks, and relevant chats—all synthesized into one easy-to-read response. It just works.
Glean MCP delivers unified knowledge discovery.
The most tedious part is the context switching. You have to remember which tool holds which piece of information, and then manually cross-reference them using their specific IDs or search filters. It's a human process that breaks down when the data gets complex.
Now, your agent handles the mapping. It doesn't matter if the document is in Confluence or Slack; as long as it’s connected through this MCP, your agent treats it like one single source of truth. You just get the answer.
What Glean MCP does for your AI
Getting accurate information used to mean logging into half a dozen different systems just to piece together the answer. Now, you can talk to your company's entire knowledge repository using natural conversation. This MCP connects directly to your enterprise account, giving your agent full visibility across all your connected applications—from project management logs to internal documents and employee profiles.
You don't need to remember where a piece of information lives. You just ask the question: 'What was our policy on Q3 marketing spend?' The MCP retrieves data from multiple sources, synthesizes an answer using advanced AI, and gives you one pure response. If you’re building your agent logic on Vinkius, this connector allows your agent to discover knowledge across apps like never before.
It handles everything from finding a specific document ID to identifying the colleague who owns that policy.
019d75a6-406b-72bf-89db-f6c9cbac8118 How to set up Glean MCP
The bottom line is you get instant access to your entire company's information, conversational style, without having to manually connect or query every single data source individually.
Subscribe to this MCP, then enter your specific Glean Domain name and API Token found in your Glean Admin Settings.
Pass these credentials to your preferred AI client, like Cursor or Claude, so it can access the corporate knowledge layer.
Ask your agent a question—like 'What was our Q2 hiring plan?'—and watch it pull synthesized answers from across all connected apps.
Who uses Glean MCP
Any knowledge worker who spends more time searching for answers than doing the actual work. If you’re tired of clicking through Slack channels, Confluence pages, and Jira tickets just to find a policy document or a person's name, this MCP is for you.
Uses the MCP to gather background context on specific projects by searching historical documentation across multiple silos in one go.
Finds employees based on skills or roles, and monitors which topics are generating discussion spikes company-wide.
Indexes custom internal documentation directly into the search system to help test AI chat workflows right from their IDE.
Benefits of connecting Glean MCP
Stop manual searching. Instead of jumping between Jira and Confluence to find one policy, you ask your agent a single question, and it returns an AI-synthesized answer pulling from all sources.
Know who owns the knowledge. Use search_people to instantly identify colleagues with specific skills or roles, skipping weeks of internal email chains to find the right expert.
Maintain context during complex tasks. The chat_completion tool keeps track of your entire conversation thread, so you don't have to restate background details when asking a follow-up question.
Keep your data clean and secure. You can use delete_document to permanently remove indexed content, ensuring compliance and removing stale records from the search results.
Build internal documentation dynamically. Indexing custom text properties allows developers to feed niche, proprietary knowledge directly into the corporate search engine for AI consumption.
Glean MCP use cases
Finding a policy that spans multiple departments
A new marketing hire needs to know the expense rules. Instead of checking HR's Confluence site, they ask their agent: 'What’s the travel budget for Q4?' The MCP uses its search_docs capability to query both finance and HR documentation, giving a consolidated answer.
Tracking down the subject matter expert
A product manager needs input on a new feature. They ask: 'Who knows about React Native in our system?' The MCP uses search_people to return two colleagues' names and titles, saving them from guessing who to bother.
Getting a summary of a large project
An executive needs the status of Project Phoenix. They ask their agent: 'What are the risks and next steps for Project Phoenix?' The MCP aggregates data by running structural extraction against multiple sources (Jira, Slack) to build a comprehensive report.
Glean MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Relying on keyword searches
The user tries to search for 'budget' in Confluence and gets 50 documents. They then have to manually check if any of those mention 'travel' or 'quarterly'.
Instead, ask your agent: 'What is the quarterly travel budget?' The MCP uses get_answer to synthesize a single answer by querying multiple sources (Confluence and Finance) at once.
Copy-pasting data between tools
A developer finds a document ID in Slack, copies it, then pastes it into Jira's search bar to see if related issues exist. This is slow and error-prone.
Use your agent to run a single query: 'Find all linked development tasks for the document with ID X.' The MCP handles the cross-platform linking automatically.
Thinking data lives in one place
The user assumes that because HR documents are on Confluence, they must contain information about developer code standards. They get a dead end.
Use the MCP to query all connected applications simultaneously for 'developer coding standards.' It checks both documentation (Confluence) and development logs (Jira/Code Repos).
When to use Glean MCP
You need this MCP if your organization’s knowledge is scattered. If you currently spend time jumping between 3 or more different apps—Slack, Notion, Confluence, etc.—to piece together a single answer, this tool solves that problem by unifying the data layer conversationally. Use it when you need context synthesis across boundaries.
Don't use this if your only need is to query one specific database (like just Jira) or if you are building an agent that already lives entirely within a single platform. If you just need raw, filtered log tracing, other category-specific tools might be better. But for true enterprise knowledge discovery across the whole stack? This is it.
Frequently asked questions about Glean MCP
How does Glean MCP search my company's data? +
The MCP connects to your enterprise account and uses its tools, like search_docs, to query multiple connected SaaS applications simultaneously. It synthesizes the results into one cohesive answer for you.
Can Glean MCP find people by skill set? +
Yes. You can use the search_people tool to look up colleagues based on their listed skills and roles, giving you direct contact information or department details.
Is this compatible with any AI agent? +
The MCP is designed to connect through Vinkius's standard Model Context Protocol (MCP). You just need your preferred AI client—Claude, Cursor, VS Code, etc.—to connect via the Vinkius platform.
Does Glean MCP only read data? +
No. In addition to reading and searching, you can actively manage knowledge by using tools like index_document or delete_document to update your corporate search index.
What if my company uses multiple versions of Confluence? +
The MCP is designed for enterprise-wide discovery. It handles the structural extraction necessary to pull accurate, relevant data across varied and complex internal knowledge sources.