AdvancedCalc Engine
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 |
|---|---|
| read_excel_metadataA | Read the structure of an Excel workbook: sheets, columns and sample rows. Read-only. |
| rolling_averageC | Calculate the rolling average of a column in a workbook sheet. |
| percentage_changeC | Calculate the percentage change of a column in a workbook sheet. |
| rolling_volatilityC | Calculate the rolling volatility (standard deviation of percentage change) of a column in a workbook sheet. |
| write_formulasA | Write Excel formulas into a workbook sheet. A backup of the workbook is created before any change. Mutating operation. |
| validate_workbookA | Validate a workbook sheet: existence of the sheet and of the expected cells. Read-only. |
| summary_statisticsA | Compute summary statistics (count, missing, mean, median, standard deviation, min, max, quartiles) for a column in a workbook sheet. Read-only. |
| correlation_matrixA | Compute the Pearson correlation matrix of numeric columns in a workbook sheet. Read-only. |
| anomaly_detectionA | Detect anomalies in a column using z-score (|z| > threshold) or IQR (1.5 * IQR bounds) methods. Read-only. |
| linear_forecastC | Fit a linear trend to a column with numpy and forecast future periods. Read-only. |
| ml_forecastA | Fit a seeded machine-learning forecaster (bagged ridge on causal lag and rolling features), evaluate it on a temporal holdout against persistence/mean/linear baselines with RMSE/MAE/MAPE/R², and forecast future periods. Falls back to the deterministic linear forecast when supervised training is not viable. Read-only. |
| ml_anomaly_detectionA | Detect anomalies in a column with a seeded machine-learning isolation forest: flags the most isolated points under a contamination rate. Optionally injects known anomalies on an in-memory copy and reports precision, recall and F1. Read-only. |
| data_quality_reportB | Produce a data quality report for a workbook sheet: per-column type, completeness, uniqueness and numeric stats. Read-only. |
| write_outputB | Write computed output rows into a workbook in the output directory. A backup is created before an existing file is overwritten. Mutating operation. |
| run_analysis_workflowA | Execute the full Architect -> Engineer -> Validator workflow for a natural-language analysis task. Generates a workflow_id, plans the analysis, executes it through the other tools, writes results into data/output when the task asks for it, independently validates the outcome, and returns a structured report. Never modifies source data. |
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 15 tools
Most tools target distinct operations, but pairs like anomaly_detection/ml_anomaly_detection and linear_forecast/ml_forecast could be confused at a glance. Detailed descriptions clarify the statistical vs. machine-learning distinction, and other tools have clear boundaries.
Names mix verb-first tools (read_excel_metadata, write_formulas, run_analysis_workflow) with descriptive noun phrases (rolling_average, percentage_change, correlation_matrix). The consistent snake_case and lowercase style keep them readable, but the lack of a uniform verb_noun pattern is a minor inconsistency.
15 tools is well-scoped for an advanced calculation engine. Each tool covers a distinct analysis, mutation, or validation capability without unnecessary bloat or obvious redundancy.
The server provides broad coverage: metadata reading, summary statistics, correlations, anomalies, forecasting, data quality, output writing, and a workflow orchestrator. Minor gaps like an explicit full-data read tool are not critical because analysis tools operate on columns directly.