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., "@timeweaver-mcp Generate 90 days of daily e-commerce sales using the ecommerce_sales preset."
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.
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.
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.
Related MCP server: misata-mcp
npx -y timeweaver-mcpAdd to your MCP client config (e.g. Claude Desktop claude_desktop_config.json):
{
"mcpServers": {
"timeweaver": {
"command": "npx",
"args": ["-y", "timeweaver-mcp"]
}
}
}To unlock Pro, add your license key:
{
"mcpServers": {
"timeweaver": {
"command": "npx",
"args": ["-y", "timeweaver-mcp"],
"env": { "TIMEWEAVER_LICENSE": "YOUR-KEY-HERE" }
}
}
}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.
"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 | 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
MIT (the server code). Pro features require a valid license key.
Generate realistic synthetic time-series data with configurable trend, seasonality, noise, anomalies, and multiple correlated series. Ideal for testing dashboards, charts, monitoring/alerting, forecasting and anomaly-detection. Output as JSON, CSV, or SQL INSERTs. Use a preset for quick sensible defaults, or specify components explicitly.
| Name | Required | Description | Default |
|---|---|---|---|
| max | No | Clamp values to this maximum. | |
| min | No | Clamp values to this minimum. | |
| seed | No | Deterministic seed for reproducible output. Pro feature. | |
| names | No | Optional explicit series names. | |
| noise | No | Noise model. 'ar1' (autocorrelated) is a Pro feature. | |
| start | No | ISO start timestamp, e.g. '2024-01-01T00:00:00Z'. Default 2024-01-01. | |
| trend | No | Trend shape. Non-linear trends are a Pro feature. | |
| format | No | Output format. 'json' (default), 'csv', or 'sql'. CSV and SQL are Pro features. | |
| length | No | Number of data points. Default 100. | |
| preset | No | Optional preset name (see list_presets). Fills sensible defaults; explicit params below override it. | |
| ar1_phi | No | AR(1) autocorrelation coefficient (-1..1), used when noise='ar1'. | |
| integer | No | Round values to integers. | |
| baseline | No | Baseline level the series varies around. | |
| anomalies | No | Injected anomalies for testing detection/alerting. Pro feature. | |
| frequency | No | Spacing between points. Default daily (or the preset's frequency). | |
| table_name | No | Table name for SQL output. Default 'timeseries'. | |
| correlation | No | Target pairwise correlation between multiple series (0..1). Pro feature. | |
| noise_level | No | Standard deviation of the noise. Default 1. | |
| seasonality | No | One or more seasonal cycles, summed together. Multiple cycles are a Pro feature. | |
| series_count | No | How many series to generate. >1 is a Pro feature. Default 1. | |
| trend_strength | No | Trend magnitude: slope per point (linear), growth rate (exponential), or capacity (logistic). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses output formats (JSON, CSV, SQL) and labels certain parameters as 'Pro features.' However, it omits behavioral details such as whether the tool is read-only (likely safe), idempotency, performance impact with large series, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single focused paragraph of four sentences, front-loading the core purpose and use cases, then detailing output formats and usage guidance. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high parameter count (21) and no output schema, the description covers the main functional aspects well. It explains what the tool generates and how to configure it. Minor gaps include missing details about return value structure and validation behavior, but overall it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds value by grouping parameters conceptually and noting 'Pro features,' but it does not explain individual parameter meaning beyond the schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate realistic synthetic time-series data' and lists configurable components (trend, seasonality, noise, anomalies, correlated series). It distinguishes from sibling tool 'list_presets' which lists preset configurations rather than generating data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Ideal for testing dashboards, charts, monitoring/alerting, forecasting and anomaly-detection.' It advises using a preset for quick defaults or specifying components explicitly. However, it does not explicitly state when not to use this tool or mention alternatives beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the built-in time-series presets (realistic ready-made configurations like e-commerce sales, server CPU, IoT temperature, website traffic, stock price, API latency). Use a preset name with generate_timeseries to get sensible defaults you can still override.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral disclosure. It correctly indicates this is a read-only listing operation, but does not detail any specific behavioral traits such as authorization requirements, ordering, or potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences, the first stating the action and the second providing usage guidance. No redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no output schema, and no annotations, the description adequately covers the purpose and usage flow. It could be more complete by describing the output format, but for such a simple tool it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the description naturally cannot add meaning beyond the schema. Per the rule, baseline is 4, and the description appropriately focuses on the tool's purpose rather than parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists built-in time-series presets, provides examples, and distinguishes it from the sibling tool generate_timeseries by explaining the intended workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to use a preset name with generate_timeseries after listing, providing clear context. It lacks explicit when-not-to-use guidance, but the intended usage is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.1generate_timeserieslist_presetsThe two tools are completely distinct: one lists predefined presets, the other generates actual time-series data. There is no overlap or ambiguity between them.
Both tool names follow the consistent verb_noun pattern with snake_case: list_presets and generate_timeseries. The naming is predictable and clear.
With only 2 tools, the server feels minimal for the described functionality (configurable trend, seasonality, noise, anomalies, multiple output formats). While focused, it could benefit from splitting generation into separate tools for configuration or output format selection.
The server covers the core use case of generating time-series data with presets, but lacks tools for creating or editing presets, which would be a natural extension. The missing capability is minor and agents can work around it by overriding parameters.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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