Glean Chatbot
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Glean ChatbotWhat are the renewal terms in the Acme contract?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Glean Chatbot
Indexes a small set of internal contracts into Glean, answers questions from those
docs via Search + Chat, and exposes the same flow as an MCP tool (ask_glean).
Requirements
Python 3.10+
A Glean instance
An Indexing API token
A Client API token with Search and Chat scopes
Related MCP server: Glean Remote MCP Server
Setup
Clone and enter the repo:
git clone <your-repo-url>
cd glean-chatbotInstall dependencies:
pip install -r requirements.txtCreate a
.envfrom the example and fill in your values:
# macOS / Linux
cp .env.example .env
# Windows (PowerShell)
copy .env.example .envVariable | Required | Purpose |
| yes | Instance name ( |
| yes | Indexing API token |
| yes | Client API token (Search + Chat) |
| yes* | User email for |
| no | Datasource name (default |
| no | Display name in the Glean UI |
| no | Regex every doc |
.env is auto-loaded by config.py (via python-dotenv). You do not need to source it.
Run
Index documents
python scripts/index_documents.py
# optional: wait until Search can find them
python scripts/index_documents.py --verifyAsk from the CLI
python scripts/ask.py "Who owns the Enronry Tony contract?"
python scripts/ask.py "What must OKLightning Tony deliver before go-live?" --top-k 3MCP (Cursor)
Project config lives at .cursor/mcp.json (absolute path to src/mcp_server.py + env vars).
Restart Cursor / reload MCP after editing it. The tool is ask_glean.
Smoke-test the server alone:
python src/mcp_server.pyTests
python tests/test_pipeline.pyNo network; mocks Search/Chat.
How it works
scripts/index_documents.py pushes data/documents.json through the Indexing API.
At query time, src/pipeline.py calls Search, then (only if there are hits) Chat with
those hits as closed-book context, and returns an answer plus sources. CLI and MCP
both call that same pipeline. Results are scoped to our own documents; if Search
returns nothing, we suggest close document titles instead of inventing an answer.
Notes / limitations
Shared sandbox ignores
datasourcesFilter; we filter client-side on the document id prefix (CUSTOM_<DATASOURCE>_Contract_).Indexing is asynchronous — docs may take a few minutes to become searchable.
Demo docs use
allowAnonymousAccess: true. Do not ship that in production.Doc
viewURLs must matchGLEAN_URL_REGEXor indexing will reject them.
See DESIGN_NOTE.md for API tradeoffs and production notes.
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If you are the server author, to access and configure the admin panel.
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