io.github.FixtureForge/timeweaver-mcp
Synthetic time-series test data with trend, seasonality, noise, and anomalies, for any MCP client.
Open source Open in the app JSON README (API)
About
Synthetic time-series test data with trend, seasonality, noise, and anomalies, for any MCP client.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- fixtureforge
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.1
- Last push
- 2026-05-30T12:41:37Z
- Repository state
- ativo
- Language
- JavaScript
- Added
- 2026-08-29 03:01:55
- Updated
- 2026-08-29 03:01:55
- Origin id
io.github.FixtureForge/timeweaver-mcp
README
# TimeWeaver MCP
**Synthetic time-series test data, on demand, inside your AI client.** Generate realistic series with configurable trend, seasonality, noise, anomalies, and multiple correlated streams — perfect for testing dashboards, charts, monitoring/alerting, forecasting models, and anomaly detection. Output as JSON, CSV, or SQL.
Part of the [fixturelab](https://github.com/FixtureForge) test-data tools. Its sibling [SeedWeaver](https://github.com/FixtureForge/seedweaver-mcp) does relational/database test data.
## Why
LLMs are unreliable at hand-generating coherent time-series — trends drift, "seasonality" doesn't actually repeat, and correlations between series are fake. TimeWeaver generates data with **verifiable statistical properties**: a linear trend really has the slope you asked for, a seasonal cycle really repeats at its period, two correlated series really hit the target correlation, and AR(1) noise really has the autocorrelation you set.
## Install
```
npx -y timeweaver-mcp
```
Add to your MCP client config (e.g. Claude Desktop `claude_desktop_config.json`):
```json
{
"mcpServers": {
"timeweaver": {
"command": "npx",
"args": ["-y", "timeweaver-mcp"]
}
}
}
```
To unlock Pro, add your license key:
```json
{
"mcpServers": {
"timeweaver": {
"command": "npx",
"args": ["-y", "timeweaver-mcp"],
"env": { "TIMEWEAVER_LICENSE": "YOUR-KEY-HERE" }
}
}
}
```
## Tools
- **`generate_timeseries`** — generate data from a preset and/or explicit components (length, frequency, baseline, trend, seasonality, noise, anomalies, correlated series). Output JSON / CSV / SQL.
- **`list_presets`** — list built-in presets: `ecommerce_sales`, `server_cpu`, `iot_temperature`, `website_traffic`, `stock_price`, `api_latency_ms`.
## Examples
> "Generate 90 days of daily e-commerce sales using the ecommerce_sales preset."
> "Generate 3 correlated server CPU series over 500 minutes with correlation 0.8, as CSV."
> "Make an hourly temperature series with a daily cycle and a level shift on day 5, as SQL into a table called readings."
## Free vs Pro
| | Free | Pro |
|---|---|---|
| Points per series | 200 | up to 100,000 |
| Series | 1 | up to many, correlated |
| Trend | none / linear | + exponential, logistic |
| Seasonality | 1 cycle | multiple cycles |
| Noise | gaussian | + AR(1) autocorrelated |
| Anomalies | – | spikes, level shifts, trend changes, dropouts |
| Output | JSON | + CSV, SQL |
| Deterministic seed | – | ✓ |
Pro: **$19/mo** or **$39 one-time** → https://fixtureforge.gumroad.com/l/timeweaver
## License
MIT (the server code). Pro features require a valid license key.