Content
# preset-mcp
MCP server for [Preset](https://preset.io) (managed Apache Superset). Manage dashboards, charts, and datasets from Claude Code and other LLM agents.
```
Claude Code ──STDIO──> preset-mcp ──> Preset API
```
## Setup for Claude Code
### 1. Get your Preset API credentials
1. Log in to [app.preset.io](https://app.preset.io)
2. Go to **Settings > API Keys**
3. Create a new token/secret pair
4. Copy both the **token** and **secret**
### 2. Install from PyPI
```bash
uv tool install preset-mcp --with preset-cli --with fastmcp --with sqlglot --with pydantic
```
### 3. Register with Claude Code
```bash
claude mcp add --scope user -e PRESET_API_TOKEN=<your-token> \
-e PRESET_API_SECRET=<your-secret> \
preset-mcp -- preset-mcp
```
To auto-connect to a specific workspace on startup:
```bash
claude mcp add --scope user -e PRESET_API_TOKEN=<your-token> \
-e PRESET_API_SECRET=<your-secret> \
-e PRESET_WORKSPACE="Your Workspace Title" \
preset-mcp -- preset-mcp
```
### 4. Verify
```bash
claude mcp list
# Should show: preset-mcp ... 63 tools
```
Then in a Claude Code session, try:
```
> list my preset workspaces
```
### Alternative: Install from source
```bash
git clone https://github.com/Evan-Kim2028/preset-mcp.git
cd preset-mcp
uv sync
claude mcp add --scope user -e PRESET_API_TOKEN=<your-token> \
-e PRESET_API_SECRET=<your-secret> \
preset-mcp -- uv run --directory /path/to/preset-mcp preset-mcp
```
## Tools (63)
### Workspace Navigation
| Tool | Purpose |
|------|---------|
| `list_workspaces` | List all workspaces you have access to |
| `use_workspace` | Switch to a workspace by title |
### Read
| Tool | Purpose |
|------|---------|
| `list_dashboards` | List dashboards (with progressive disclosure) |
| `get_dashboard` | Get detail for a single dashboard (supports `response_mode`) |
| `list_charts` | List charts |
| `get_chart` | Get detail for a single chart (supports `response_mode`) |
| `list_datasets` | List datasets |
| `get_dataset` | Get detail for a single dataset (columns, metrics, SQL) |
| `list_databases` | List database connections |
| `get_database` | Get detail for a single database connection |
| `workspace_catalog` | Relationship-aware topology map |
### Create
| Tool | Purpose |
|------|---------|
| `create_dashboard` | Create a new empty dashboard |
| `create_dataset` | Register a SQL query as a virtual dataset |
| `create_chart` | Build a chart from a dataset |
### Update
| Tool | Purpose |
|------|---------|
| `update_dataset` | Change a dataset's SQL, name, or description |
| `update_chart` | Change a chart's title, viz type, or parameters |
| `update_dashboard` | Rename or publish/unpublish a dashboard |
### Dashboard Lifecycle
| Tool | Purpose |
|------|---------|
| `export_dashboard` | Export a dashboard ZIP bundle for backup or migration |
| `import_dashboard` | Import a dashboard ZIP bundle and report affected dashboard IDs |
| `delete_dashboard` | Delete a dashboard after exporting a backup ZIP |
### SQL & Query
| Tool | Purpose |
|------|---------|
| `run_sql` | Execute a read-only SQL query through Preset's connection |
| `query_dataset` | Query a dataset using Superset's metric/dimension abstraction |
### Validation & Audit
| Tool | Purpose |
|------|---------|
| `validate_chart` | Validate a single chart via chart-data execution |
| `validate_dashboard` | Validate all charts on a dashboard |
| `validate_chart_render` | Validate chart rendering via headless browser probe |
| `validate_dashboard_render` | Validate render status across dashboard charts |
| `verify_chart_workflow` | One-shot chart→dashboard query/render verification |
| `verify_dashboard_structure` | Validate dashboard layout graph and chart references |
| `verify_dashboard_workflow` | One-shot dashboard structure/query/render verification |
| `repair_dashboard_chart_refs` | Repair stale dashboard chart ID references |
| `list_mutations` | Inspect local mutation audit journal entries |
| `list_dashboard_snapshots` | List local pre-mutation dashboard snapshots |
| `restore_dashboard_snapshot` | Restore dashboard layout/settings from local snapshot |
| `capture_dashboard_template` | Capture reusable dashboard+chart template JSON |
| `capture_golden_templates` | Batch-export templates from dashboard IDs |
| `snapshot_workspace` | Full inventory dump for auditing |
## Typical Workflow
The intended workflow pairs preset-mcp with a data warehouse MCP (like [igloo-mcp](https://github.com/Evan-Kim2028/igloo-mcp) for Snowflake):
```
1. Explore data in Snowflake (igloo-mcp)
2. Write and validate your SQL (igloo-mcp)
3. workspace_catalog (preset-mcp) — understand what exists
4. list_databases (preset-mcp) — find the database_id
5. create_dataset (preset-mcp) — register the SQL
6. create_chart + create_dashboard (preset-mcp) — build the viz
7. update_dataset / update_chart (preset-mcp) — iterate
```
## Features
### Progressive Disclosure
All list and detail tools accept a `response_mode` parameter to control token usage:
- **`compact`** — IDs and names only (~80% fewer tokens)
- **`standard`** — Key metadata fields (default for list tools)
- **`full`** — Raw API response (default for detail tools)
```
list_dashboards(response_mode="compact")
→ {"count": 42, "data": [{"id": 1, "dashboard_title": "Revenue"}, ...]}
get_dashboard(dashboard_id=80, response_mode="standard")
→ key fields only, no position_json or json_metadata blobs
```
Detail tools (`get_dashboard`, `get_chart`, `get_dataset`, `get_database`) default to `full` for backward compatibility. Use `standard` or `compact` to avoid large payloads — dashboards with 20+ charts can return 50-100K chars in full mode.
### SQL Safety
`run_sql` uses [sqlglot](https://github.com/tobymao/sqlglot) for AST-based validation:
- Blocks write operations (INSERT, UPDATE, DELETE, DROP, ALTER, MERGE, TRUNCATE, GRANT, REVOKE)
- Detects multi-statement injection (`SELECT 1; DROP TABLE x`)
- Handles comment-wrapped bypasses (`-- comment\nDELETE FROM x`)
- Catches CTE-wrapped writes (`WITH x AS (...) DELETE FROM y`)
### Structured Errors
Errors include `error_type` and `hints[]` so the LLM can self-recover:
```json
{
"error": "No workspace selected.",
"error_type": "no_workspace",
"hints": [
"Call list_workspaces to see available workspaces.",
"Then call use_workspace('Title') to select one."
]
}
```
### Structured Logging
JSON logs on stderr (stdout is reserved for the STDIO transport):
```json
{"ts":"2025-02-11 12:00:00","level":"INFO","msg":"tool=list_dashboards status=ok duration_ms=234"}
```
## Configuration
All settings are overridable via environment variables:
| Variable | Default | Purpose |
|----------|---------|---------|
| `PRESET_API_TOKEN` | (required) | Preset API token |
| `PRESET_API_SECRET` | (required) | Preset API secret |
| `PRESET_WORKSPACE` | (optional) | Auto-connect to this workspace |
| `PRESET_MCP_SQL_ROW_LIMIT` | `1000` | Max rows from SQL queries |
| `PRESET_MCP_SQL_SAMPLE_ROWS` | `5` | Rows shown in standard mode |
| `PRESET_MCP_TRUNCATION_THRESHOLD` | `50` | Full-mode truncation cutoff |
| `PRESET_MCP_TRUNCATION_TAIL` | `5` | Tail rows kept when truncating |
| `PRESET_MCP_LOG_LEVEL` | `INFO` | Logging verbosity |
## Python Library
preset-mcp also works as a standalone Python library (no MCP required):
```python
from preset_py import connect
ws = connect("My Workspace")
dashboards = ws.dashboards()
df = ws.run_sql("SELECT * FROM revenue LIMIT 10", database_id=1)
ws.create_dataset("daily_revenue", "SELECT ...", database_id=1)
ws.create_chart(dataset_id=5, title="Revenue", viz_type="echarts_timeseries_bar")
```
## Advanced Recipe: Pie Chart with Ad-hoc Metric
Use `params_json` for advanced chart params such as ad-hoc filters.
```json
{
"dataset_id": 868,
"title": "USDSUI Distribution",
"viz_type": "pie",
"metrics": "[{\"expressionType\":\"SQL\",\"sqlExpression\":\"AVG(AMOUNT_USD)\",\"label\":\"AVG(AMOUNT_USD)\"}]",
"groupby": "[\"CATEGORY\",\"SOURCE_NAME\"]",
"params_json": "{\"adhoc_filters\":[{\"col\":\"TOKEN_SYMBOL\",\"op\":\"==\",\"val\":\"USDSUI\"}]}"
}
```
Notes:
- `create_chart.metrics` accepts saved metric names or ad-hoc metric objects.
- `create_chart.template="auto"` applies viz-specific defaults for missing fields.
- `params_json` is validated preflight against dataset columns/metrics.
- `params_json` cannot include datasource-rebinding keys like `viz_type` or `datasource_id`.
- `create_chart.repair_dashboard_refs` defaults to `false` so chart creation does not mutate dashboard layouts unless explicitly requested.
## Strict Params Semantics
- `update_chart(params_json=...)` uses strict validation semantics and treats `params_json` as a full viz-compatible params payload.
- For viz types with required fields (for example `pie` and timeseries charts), partial payloads like only `{"color_scheme":"..."}` are rejected.
- Use `get_chart(chart_id=<id>, response_mode="full")` to copy/edit the existing params JSON when you need precise updates.
## Golden Template Workflow
Use proven dashboards (for example BTC Fight, Walrus, DeepBook) as template sources:
1. Find dashboard IDs:
```text
list_dashboards(response_mode="compact")
```
2. Verify layout/query/render health before templating:
```text
verify_dashboard_workflow(dashboard_id=<id>, include_render=true, response_mode="standard")
```
3. Export a single reusable template:
```text
capture_dashboard_template(
dashboard_id=<id>,
portable=true,
include_query_context=false,
include_dataset_schema=true,
output_path="~/.preset-mcp/golden-templates/<name>.json"
)
```
4. Export multiple dashboards in one run:
```text
capture_golden_templates(
dashboard_ids="[80,97,162]",
output_dir="~/.preset-mcp/golden-templates",
portable=true,
include_dataset_schema=true
)
```
CLI alternative:
```bash
uv run scripts/export_golden_templates.py \
--workspace "Mysten Labs--General" \
--dashboard-ids 80,103,102 \
--output-dir ~/.preset-mcp/golden-templates \
--overwrite
```
Optional live smoke test (skipped by default):
```bash
PRESET_MCP_ENABLE_LIVE_TESTS=1 \
PRESET_MCP_LIVE_DASHBOARD_IDS=80,103,102 \
uv run --with pytest pytest -q tests/test_live_dashboard_smoke.py
```
## License
MIT
