timeweaver-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| TIMEWEAVER_LICENSE | No | Your TimeWeaver Pro license key to unlock additional features |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_presetsA | 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. |
| generate_timeseriesA | 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The 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.