Glean MCP. Search every company app from one chat window.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Glean. Connect your corporate knowledge base to any AI client. Search across all your SaaS apps—Jira, Confluence, Slack, CRM—and get AI-generated answers instantly.
You can search people by role, find documents across departments, or even delete old index entries. It manages corporate knowledge discovery and AI chat from one place.
What your AI agents can do
Autocomplete
Predicts potential page destinations based on partial prefixes, helping you quickly navigate deep within the knowledge base.
Chat completion
Sends an automated validation check against the Gateway history to maintain conversational context and thread integrity.
Custom request
Allows running custom POST requests to identify specific active arrays across various native data parsing logic.
Searches across multiple connected applications (Jira, Confluence, etc.) to find relevant documents and data records.
Runs Retrieval-Augmented Generation (RAG) to produce summarized answers based only on your company's internal documentation.
Queries corporate directories using natural language to list colleagues who match specific skills, roles, or names.
Takes custom text properties and uploads them into the search index, making them discoverable by the AI.
Handles complex, multi-turn conversations, remembering previous inputs and outputs for better reasoning.
Removes documents from the index permanently, preventing the AI from ever retrieving them again.
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Supported MCP Clients
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Glean MCP Server: 10 Tools for Enterprise Search
These tools give your AI agent full control over corporate data, allowing it to search, index, retrieve, and manage records across multiple applications.
019d75a6autocomplete
Predicts potential page destinations based on partial prefixes, helping you quickly navigate deep within the knowledge base.
019d75a6chat completion
Sends an automated validation check against the Gateway history to maintain conversational context and thread integrity.
019d75a6custom request
Allows running custom POST requests to identify specific active arrays across various native data parsing logic.
019d75a6delete document
Permanently removes indexed documents and blocks future retrieval by executing a targeted deletion command.
019d75a6get answer
Exports active billing information by running structured rules to generate a direct, synthesized answer.
019d75a6get suggestions
Extracts rich Churn flags by running validations that signal potential issues or areas needing review.
019d75a6index document
Uploads custom content to the system, making the text available for search and retrieval by the AI.
019d75a6search datasource
Performs structural property extraction by querying specific data sources and running account-related logic.
019d75a6search docs
Searches for bounded CRM records within the Headless Glean Platform, providing structured document results.
019d75a6search people
Retrieves structured JSON payloads containing hard customer bindings by searching the corporate directory.
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 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ 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
What you can do with this MCP connector
Glean. You hook up your company's entire knowledge base to any AI client, and it works. You search everything—Jira, Confluence, Slack, your CRM—and get answers generated by AI, based only on your internal docs. You can look up people by role, find documents across departments, or even wipe old index entries.
It handles all your corporate knowledge discovery and AI chat in one spot. Here's what you can do: You use search_docs to find structured CRM records inside the Headless Glean Platform. You run search_people to get structured JSON payloads of hard customer bindings by checking the corporate directory. You call autocomplete to predict potential page destinations when you type partial prefixes, helping you navigate the deep knowledge base fast.
You use get_answer to export active billing info by running structured rules that generate a direct, synthesized answer. You call get_suggestions to pull rich Churn flags by running validations that signal potential problems or areas that need reviewing. You run search_datasource to perform structural property extraction by querying specific data sources and running account-related logic.
You use index_document to upload custom content, making that text available for search and retrieval by your AI client. You use delete_document to permanently remove indexed documents, blocking future retrieval by running a targeted deletion command. You use chat_completion to send an automated validation check against the Gateway history, which keeps the conversational context and thread intact.
You use custom_request to run custom POST requests, identifying specific active arrays across various native data parsing logic. You've got all your company data, and this tool makes sure your AI client can actually use it.
How Glean MCP Works
- 1 Subscribe to the Glean MCP Server and provide your Glean Domain and API Token.
- 2 Your AI client connects and authenticates the connection to your corporate knowledge graph.
- 3 You ask a question (e.g., 'What is the expense policy for Q3?'). The server executes the appropriate tools to search, retrieve, and synthesize the final answer.
The bottom line is, your AI agent becomes a full-stack corporate search engine, pulling answers from every connected app.
Who Is Glean MCP For?
Anyone who spends time searching for documents, policies, or people across multiple disconnected company apps. If you're tired of copy-pasting links or asking different people for the same policy, this is for you. It's for the knowledge worker who needs a single source of truth.
Uses the server to find niche documentation by filtering across multiple data sources (e.g., 'Show me Jira tickets related to the Confluence spec for Project X').
Uses the People & Identity Discovery tool to find employee contact details or skills matching a job description without leaving the chat interface.
Uses the Knowledge Ingestion & Indexing tool to test and upload custom documentation directly into the AI chat workflow for validation.
What Changes When You Connect
- Instantly find answers across every app. Instead of opening Jira, Confluence, and Slack separately, let the AI run the search_docs and search_datasource tools to synthesize the answer in one go.
- Pinpoint people fast. Use the search_people tool to find colleagues by skill or role. You get back their name, title, and contact info without manual directory lookups.
- Stop losing data. Use the delete_document tool to permanently remove outdated or sensitive documents from the index, blocking future retrievals and keeping your knowledge clean.
- Build custom data sources. The index_document tool lets you upload proprietary text, routing it directly into the corporate search logic so the AI can read it.
- Maintain complex conversations. The chat_completion tool tracks your conversation history, so the AI remembers what you asked 10 turns ago when answering your follow-up question.
- Audit your data. Use get_suggestions to extract rich Churn flags or get_answer for billing data, turning raw search results into actionable business metrics.
Real-World Use Cases
Finding the latest expense policy.
A new hire asks their agent: 'What's the current travel expense policy?' The agent runs the search_docs tool across Confluence and the search_datasource tool across the HR portal. It returns an AI-generated summary, citing the exact policy document and dollar limits, solving the problem in seconds.
Identifying the subject matter expert.
A project manager needs a dev who knows React Native. They ask: 'Who knows about React Native in our company?' The agent runs the search_people tool, returning a list of two colleagues, their roles, and contact details. The PM then asks for the most recent Slack activity for one of them.
Adding undocumented processes to search.
The compliance officer discovers a manual process that needs to be searchable. They use the index_document tool to upload the policy text. The system indexes it, and now the AI can answer questions about this niche process just like it answers questions about core HR documents.
Cleaning up old, irrelevant data.
The data governance team identifies several decommissioned policy documents. They run the delete_document tool on those documents. This permanently removes them from the index, ensuring that the AI never accidentally retrieves and cites outdated information again.
The Tradeoffs
Copying/Pasting document links
Searching for 'Q2 plan' by opening Google Drive, then Confluence, then Jira, and manually copy-pasting three different links into a chat window for the agent to read.
→ Just ask the question once. Let the AI agent run search_docs and search_datasource. It gathers knowledge from all sources and presents a single, synthesized answer.
Overloading the agent with context
Trying to cram too many niche requests into one prompt: 'Find the person, search the document, and also delete the old ticket.' The agent gets confused and fails to execute the correct sequence.
→ Break it up. Use the dedicated tools sequentially. First, run search_people. Then, use search_docs on the returned document ID. Finally, if needed, use delete_document on the stale ID.
Relying on keyword matching alone
Asking 'What is our policy?' and getting a list of 15 links, forcing the user to read all of them to find the answer.
→ Ask the question, and let the agent run get_answer. It reads all the search results and synthesizes the final policy answer for you.
When It Fits, When It Doesn't
Use this server if your primary problem is knowledge fragmentation. You need an AI agent that can treat your entire company—every SaaS app, every document, every employee record—as one single searchable database. You need multi-step reasoning: finding a person, getting their department, and then finding the relevant document they created.
Don't use this if your goal is simple, single-purpose API calls (e.g., only validating a schema). For that, a dedicated, specialized API gateway is better. If you only need to run a basic database query without context, use a simple data connector instead of the full Glean suite.
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 server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Searching for company knowledge shouldn't feel like logging into five different apps.
Today, finding a single answer requires jumping through hoops. You start on Confluence for the policy, then switch to Jira to see the implementation tickets, then maybe open Slack to see who owns the decision. You end up copying three different links and pasting them into a chat window, hoping the AI figures out the connection.
With the Glean MCP Server, you just ask the question once. The agent runs the search_docs tool and the search_datasource tool behind the scenes. It pulls the policy from Confluence, the status from Jira, and the owner from the directory, and gives you one synthesized, cited answer.
Glean MCP Server: Get actionable data, not just links.
Manual data extraction is a nightmare. You might need to run a search to find a record, then copy the ID, then run a second query to get the full details, and then run a third process to delete the old record. It's a painful, multi-step process that requires deep knowledge of APIs.
Now, the AI agent handles the orchestration. You tell it what to do—'Find the best person and delete their old records.' It runs search_people, uses the result, and then executes the delete_document tool. It's automated.
Common Questions About Glean MCP
How does Glean MCP Server handle searching documents using search_docs? +
The search_docs tool looks for structured records within the Headless Glean Platform. It returns specific document IDs and bounded CRM records, so you know exactly what information you're getting.
Can I use search_people to find a colleague's skills? +
Yes. The search_people tool queries corporate directories to match user skills, roles, and names. It returns a structured JSON payload with hard customer bindings.
Is it safe to use delete_document? +
Yes, but it's irreversible. The delete_document tool permanently removes indexed documents and blocks all future retrieval. Use it only when you are absolutely sure the data is obsolete.
What is the difference between search_datasource and search_docs? +
search_docs handles general CRM records. search_datasource performs structural property extraction by targeting specific data sources and running account logic.
How do I get an AI answer using get_answer? +
You prompt the agent with a question, and the get_answer tool fires RAG mechanisms. It returns a pure, AI-generated block of text synthesized only from your company's data.
How does `autocomplete` help predict search results? +
It predicts precise page destinations using partial prefixes. This tool executes /autocomplete to give you instant suggestions, so you don't have to type the full search term.
What kind of data does `search_people` provide? +
It returns a JSON payload with hard customer bindings. This allows you to match user names, roles, and skills directly against your corporate active directory.
How do I manage ongoing chat history with `chat_completion`? +
It manages ongoing text streams while maintaining historical thread mapping. This means your agent remembers the context of the conversation, even across multiple turns.
Can my agent get direct AI answers based on my company's data via Glean? +
Yes. Use the 'get_answer' tool. It fires Glean's internal RAG mechanisms to retrieve a pure AI-generated response distilled from your indexed company knowledge flawlessly.
How do I search for experts in my company by their skills via chat? +
Use the 'search_people' tool. The agent will retrieve information from your corporate active directory, matching user profiles, skills, and names to help you find the right colleague natively.
Can I index my own custom documents in Glean through the agent? +
Absolutely. Use the 'index_document' tool. Provide a JSON object defining the ID, Title, and text content. The agent will route the payload into Glean's search logic synchronously.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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