
Timeweaver Mcp
io.github.FixtureForgePublished by GitHub account FixtureForgev0.1.1Updated Oct 24, 2026
Synthetic time-series test data with trend, seasonality, noise, and anomalies, for any MCP client.
Overview
Generates synthetic time-series test data with trend, seasonality, noise and anomalies, returned as JSON, CSV or SQL for any MCP client.
- What you can do
- Generate time-series data from built-in presets or explicit components such as length, frequency, baseline, trend, seasonality, noise, anomalies and correlated series
- List the built-in presets (e-commerce sales, server CPU, IoT temperature, website traffic, stock price, API latency)
- Emit the generated series as JSON, CSV or SQL statements
- When to use it
- Useful when you need realistic, statistically coherent sample series for dashboards, charts, monitoring and alerting tests, forecasting models or anomaly detection, without hand-writing fake data.
- Requirements
- Runs locally over stdio, started with npx, so Node.js and network access to fetch the npm package are needed. No accounts or credentials are required for the free tier; a license key in the TIMEWEAVER_LICENSE environment variable unlocks Pro features.
Installation
In SourceWeft
- Open Timeweaver Mcp in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
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 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
Add to your MCP client config (e.g. Claude Desktop claude_desktop_config.json):
To unlock Pro, add your license key:
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
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.
Source: README.md at commit 74e3d60
Tools
0Version history
1- v0.1.1LatestSep 16, 2026
Score
40% of the applicable checks met
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