Your work knowledge agent. Use Glean chat to answer any question about the user's company, accounts, colleagues, meetings, documents, or work history. Glean synthesizes across 100+ enterprise apps and always cites sources.
/plugin marketplace add ken-cavanagh-glean/fieldkit/plugin install glean-mcp@fieldkitThis skill inherits all available tools. When active, it can use any tool Claude has access to.
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.
Glean has indexed the user's entire work context:
Glean synthesizes across all these sources. It doesn't just search — it thinks and answers.
chatchat(message="your question here")
chat(message="follow-up question", context=["previous response"])
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?")
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?")
Under the hood, the Glean agent has access to specialized tools. You don't invoke these directly — Glean decides when to use them:
| Tool | What It Does |
|---|---|
| Search | Finds documents across all indexed sources |
| People Lookup | Queries the employee directory and org structure |
| Email Search | Searches Gmail with filters (from, to, labels) |
| Calendar Lookup | Finds meetings and calendar events |
| Document Reader | Retrieves full content from URLs |
| Code Search | Searches internal repositories |
| Activity Tracker | Shows what the user worked on recently |
Glean orchestrates these automatically based on your question.
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"])
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?"
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]
)
Every response includes source links. These are real — use them to verify or dive deeper.
| Situation | Ask Glean |
|---|---|
| Starting work on an account | "Account overview for X" |
| Preparing for a meeting | "Prep me for my meeting with X" |
| Researching a person | "What do I know about X?" |
| Understanding a project | "What's the status of X?" |
| Finding an expert | "Who knows about X?" |
| Recalling a decision | "What was decided about X?" |
| Writing a summary | "Summarize my activity on X" |
| Investigating an issue | "What's the history of X issue?" |
| Need | Use Instead |
|---|---|
| Public/external information | Web search |
| Local project files | Read tool |
| Info already in conversation | Reference it directly |
| Real-time data | Glean indexes periodically |
| Speculation/opinion | Your own reasoning |
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.
# 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.")
# 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.")
# Debugging an issue
chat(message="What do we know about the agent error Adam Fowler reported? Include request IDs and any support ticket context.")
# Start of week
chat(message="What are my priorities this week based on my calendar, recent activity, and outstanding tasks?")
# 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.")
Glean exists to reduce cognitive load. Instead of:
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.
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