Timeweaver Mcp
Generates synthetic time-series data with verifiable statistical properties instead of relying on LLMs to eyeball trends and cycles. Exposes two tools: generate_timeseries lets you build series from presets (ecommerce_sales, server_cpu, stock_price) or explicit components like trend slope, seasonal periods, AR(1) noise, and injection points for anomalies. list_presets shows the built-ins. Free tier gives you 200 points and basic trend/seasonality. Pro unlocks correlated multi-series, exponential/logistic growth, multiple seasonal cycles, anomaly types (spikes, level shifts, dropouts), and CSV/SQL output. Reach for this when you need realistic test data for dashboards, alerting logic, or forecast models where hand-wavy fake numbers will bite you later.
- tools
- 2
- transport
- STDIO
- last updated
- May 30, 2026
Tools
2 toolsPublic tool metadata for what this MCP can expose to an agent.
generate_timeseriesGenerate data from a preset and/or explicit components (length, frequency, baseline, trend, seasonality, noise, anomalies, correlated series). Output JSON / CSV / SQL.
Generate data from a preset and/or explicit components (length, frequency, baseline, trend, seasonality, noise, anomalies, correlated series). Output JSON / CSV / SQL.
No parameter schema in public metadata yet.
list_presetsList built-in presets: ecommerce_sales, server_cpu, iot_temperature, website_traffic, stock_price, api_latency_ms.
List built-in presets: ecommerce_sales, server_cpu, iot_temperature, website_traffic, stock_price, api_latency_ms.
No parameter schema in public metadata yet.
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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 test-data tools. Its sibling SeedWeaver 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):
Details
- Repository
- github.com/fixtureforge/timeweaver-mcp
- Category
- Data & Analytics
- Package
timeweaver-mcp- Transport
- STDIO
- Registry
- MCP registry, active
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