lumen-mcp
Provides tools to connect to DuckDB databases, execute SQL queries, and manage tables within a DuckDB workspace.
Allows using OpenAI's API (via LLM key) to power Lumen's agents for SQL generation and chart building.
Provides tools to render Vega-Lite specs into charts, supporting both PNG and interactive HTML output.
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., "@lumen-mcpconnect to sample.db and show sales by region as a bar chart"
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.
lumen-mcp
Drive Lumen's data to SQL to chart to report loop from any MCP client (Claude Code, Claude Desktop, Cursor, VS Code, Goose, ...).
lumen-mcp is a standalone MCP server. It imports Lumen as a dependency and reuses Lumen's own
engine; it does not modify Lumen. A couple of not-yet-public Lumen helpers are reached via a small
_shims.py, which picks up the public API automatically once the installed Lumen exposes it.
Two modes
Keyless (default, no API key). The host LLM you are already talking to writes the SQL and the Vega-Lite spec; lumen-mcp runs them through Lumen (DuckDB workspace, spec normalization, rendering, report export). The host is the agent.
Keyed (opt-in). Lumen's own
SQLAgent/VegaLiteAgent/Plannerrun inside the server. You just describe what you want. Requires an LLM key (see below).
Same tools, same DuckDB workspace, same chart/report output. The key just flips the brain.
Related MCP server: AskTable MCP Server
The session is a DuckDB workspace
Each SQL result is materialized as a real table (via Lumen's
DuckDBSource.create_sql_expr_source(materialize=True)), so results accrete in one connection and
you reference them by table name. Charts and reports bind to those tables.
Keyless tools
connect_source(uri, name?)- connect a.db/.duckdb,.csv,.parquet,.json, or:memory:.list_tables()/describe_table(table)- schema + a small sample.run_sql(sql, name?)- execute; the result becomes tablename; returns columns + sample.render_vegalite(spec, table)- normalize the spec, render; returns an inline PNG plus saved PNG/HTML paths and aui_uri.refine_chart(chart_id, spec_patch)- deep-merge a patch and re-render under the same id.get_chart(chart_id)/list_charts()- fetch or list rendered charts.view(target)- show a chart (by id) or a saved.pnginline; HTML files return a path to open.build_report(items, title, formats?)- assemble charts + markdown into a self-contained HTML and a reproducible.ipynb; returns inline chart previews too.save_session(path)/load_session(path)- persist and restore the workspace and its charts.launch_dashboard()/stop_dashboard()- serve the session's charts + tables as a live, interactive Lumen dashboard (a backgroundpanel serveprocess) at a localhost URL.
Charts are also served as ui://lumen/chart/{id} MCP-App resources (interactive HTML) for
Apps-capable hosts (Claude Desktop/web).
Keyed mode (Lumen's own agents)
Start the server with an LLM key in the environment and one extra tool appears:
OPENAI_API_KEY=... lumen-mcp # or ANTHROPIC_API_KEY=...lumen_ask(prompt)- Lumen's own Planner + SQLAgent + VegaLiteAgent run headless over the workspace: Lumen writes and runs the SQL and builds the chart itself. Returns the chart inline plus the generated SQL and a summary.
Set LUMEN_MCP_LLM_MODEL to override the default model (gpt-4o / claude-sonnet-4-5).
You can also enable keyed mode at runtime without restarting:
set_llm_key(api_key, provider, model?)- configure a key mid-session (it passes through the conversation, so prefer the env var for anything sensitive and rotate afterward).ui://lumen/setup- an in-chat key-entry pane on Apps-capable hosts (Claude Desktop/web) that submits the key without routing it through the model.
Until a key is configured, lumen_ask returns a clear "not configured" message.
Live dashboard
launch_dashboard() runs a Panel server (inside lumen-mcp, reusing the panel-live-server pattern)
that reconstructs the session's charts and tables into a live, interactive dashboard and returns a
http://localhost:PORT/... URL. Unlike the static HTML export, its widgets and tables re-query the
DuckDB workspace live. stop_dashboard() shuts it down. Requires a local browser (localhost).
Quick start
pip install -e .
python examples/make_sample_db.py # writes sample.db
# register with your client, e.g.:
# claude mcp add lumen-mcp -- lumen-mcpThen, in the client: connect to sample.db, run a GROUP BY query, and render a bar chart.
Development
pip install -e ".[dev]" # editable install with pytest
pytest # run the tests
ruff check src tests examplesTests: test_slice (keyless logic), test_roundtrip (MCP protocol), test_dashboard (spawns a live
server), test_keyed (skips unless an LLM key is set).
Status
Keyless loop + delivery hardening + live dashboard + keyed agentic mode (15 tools). See CHANGELOG.md for details.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ghostiee-11/lumen-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server