Echo
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_seriesA | Find explainable anomalies and recurring patterns in a time series using the Matrix Profile algorithm. Each anomaly is explained by showing its nearest match elsewhere in the series (or the lack of one), not just flagged as a bare score. Args: values: The time series as a flat list of numbers, in order. subsequence_length: The length of the pattern window to compare (e.g. 12 for a daily cycle sampled hourly). Must be at least 3 and at most half the series length. top_k: How many top anomalies/patterns to report. |
| analyze_csvB | Same as analyze_series, but loads the values from a numeric column of a CSV file. Args: file_path: Absolute path to the CSV file. column: Name of the numeric column to analyze. subsequence_length: Pattern window length (see analyze_series). top_k: How many top anomalies/patterns to report. |
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 distinguishable by input source (in-memory list vs. CSV file path), so an agent can pick correctly. However, analyze_csv is explicitly documented as 'same as analyze_series' but with a file loader, meaning they are functionally the same operation with overlapping purpose and only a thin input-format boundary.
Both names follow a clean, predictable verb_noun snake_case pattern (analyze_series, analyze_csv) with a shared 'analyze_' prefix that signals the common family. No deviation or mixed convention.
With only two tools, the surface is thin — it amounts to one core algorithm exposed through two input adapters. That is borderline minimal rather than a well-rounded set, though the narrow scope keeps it from being egregious.
For time-series anomaly/pattern discovery the pair does cover list and CSV inputs, but there are notable gaps: no output for the raw Matrix Profile, no way to write/export results, and no multi-column or batch analysis. Agents can work around these but the lifecycle feels partial.