Glean MCP

Glean: Your Work Knowledge Agent

Glean is an AI agent with deep context about the user's work — think of it as an oracle for enterprise knowledge. When you're stuck, need background, or want to understand something about the user's company, accounts, colleagues, or work history — ask Glean.

What Glean Knows

Glean has indexed the user's entire work context:

• Communications: Slack messages, email threads, meeting transcripts
• Documents: Google Drive, Confluence, Notion, SharePoint
• Code: GitHub repos, pull requests, commits
• People: Employee directory, org charts, contact info
• CRM: Salesforce accounts, opportunities, contacts
• Tickets: Jira issues, support cases
• Meetings: Calendar events, Gong recordings, Gemini notes
• 100+ enterprise apps connected and indexed

Glean synthesizes across all these sources. It doesn't just search — it thinks and answers.

Core Tool: chat

chat(message="your question here")
chat(message="follow-up question", context=["previous response"])

What Chat Does

1. Understands your question — natural language, complex queries, multi-part asks
2. Searches across all indexed sources — not just keyword matching
3. Synthesizes an answer — connects dots across documents, conversations, people
4. Returns cited sources — every claim links back to source documents

Chat Knows Who the User Is

Glean is identity-aware. It knows the authenticated user automatically:

# These just work — no need to specify the user's name or email
chat(message="What am I working on?")
chat(message="Who is my manager?")
chat(message="What meetings do I have today?")
chat(message="What did I discuss with Jane last week?")

What to Ask Glean

Account & Customer Research:

chat(message="Give me an account overview for MongoDB")
chat(message="Who are the key contacts at Tenstorrent?")
chat(message="What's the deal status for Sports Facilities Advisory?")
chat(message="What use cases is Ratio Therapeutics exploring?")

People & Org Questions:

chat(message="Who works on the agent builder team?")
chat(message="Who should I talk to about MCP integrations?")
chat(message="What's Josh Rutberg's background?")

Process & Policy:

chat(message="How do I escalate a support ticket?")
chat(message="What's the onboarding process for new accounts?")
chat(message="How does the AIOM role differ from CSM?")

Historical Context:

chat(message="What happened in my last meeting with Adam Fowler?")
chat(message="What was decided about the OCR issue at SFC?")
chat(message="What's the history of the MongoDB account?")

Synthesis & Strategy:

chat(message="What are the common blockers for agent adoption?")
chat(message="What patterns do successful agent deployments share?")
chat(message="How do other AIOMs handle high-touch accounts?")

Glean's Tools

Under the hood, the Glean agent has access to specialized tools. You don't invoke these directly — Glean decides when to use them:

Glean orchestrates these automatically based on your question.

How to Use Glean

1. Ask First, Drill Down Later

Always start with chat. If you need more detail, ask follow-up questions:

# Start broad
chat(message="What's the status of the Tenstorrent account?")

# Then drill down
chat(message="What specific use cases are they exploring?",
     context=["previous response about Tenstorrent"])

2. Be Specific

Glean is smart, but specificity helps:

Less effective: "Tell me about MongoDB"
More effective: "What are the current active projects with MongoDB and who are the key stakeholders?"

3. Multi-Turn Conversations

Use the context parameter for follow-ups:

response1 = chat(message="What meetings do I have with Ratio Therapeutics?")
response2 = chat(
    message="What should I prepare for the next one?",
    context=[response1]
)

4. Trust the Citations

Every response includes source links. These are real — use them to verify or dive deeper.

When to Ask Glean

When NOT to Use Glean

Limitations

Indexing lag: New documents may take minutes to hours to appear.

Permission-scoped: Glean only sees what the user has access to. If results seem sparse, the user may lack permissions.

Structured data: Returns markdown/snippets, not raw CSVs. For full spreadsheet analysis, have the user upload the file.

External companies: Glean knows about the user's company's interactions with external companies (emails, meetings, CRM data) but doesn't have access to their internal systems.

Examples in Context

Meeting Prep

# Before a customer call
chat(message="Prep me for my call with Adam Fowler at Sports Facilities. Include recent context, open issues, and what we discussed last time.")

Account Research

# New account handoff
chat(message="Give me a full briefing on the Golden Gate Bridge account — adoption status, key contacts, risk factors, and what the previous AIOM was working on.")

Problem Investigation

# Debugging an issue
chat(message="What do we know about the agent error Adam Fowler reported? Include request IDs and any support ticket context.")

Weekly Planning

# Start of week
chat(message="What are my priorities this week based on my calendar, recent activity, and outstanding tasks?")

People Context

# Before a 1:1
chat(message="What should I know before my 1:1 with Josh Rutberg? Include recent discussions and any items I should bring up.")

Philosophy

Glean exists to reduce cognitive load. Instead of:

• Searching Slack, then Drive, then Salesforce, then email...
• Trying to remember which tool has what...
• Manually synthesizing across sources...

Just ask Glean. It handles the complexity. You get the answer.

This is the "second brain" pattern — an AI agent with deep context about your work, always available to consult when you need to understand, remember, or decide.

When in doubt, ask Glean.