operational-data-mcp
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": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_assetsB | List all machines / production lines in the operational dataset, with their metadata (type, line, target throughput). |
| query_productionB | Return hourly production counts, optionally filtered by asset id and/or time range. Time range is ISO 8601 timestamps. |
| query_downtimeC | Return downtime events, optionally filtered by asset id, time range, or cause code. |
| summarize_throughputA | Roll up production counts vs. target throughput per asset. Returns total produced, target, attainment ratio, and downtime minutes per asset over the (optionally bounded) time window. |
| find_anomaliesA | Run simple z-score anomaly detection on production counts. Returns timestamps where an asset's hourly count is more than |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| assets | List of machines / production lines in the dataset. |
| production | Hourly production counts by asset. |
| downtime | Downtime events by asset with cause codes. |
TDQS
Scored across 5 tools
Each tool targets a distinct purpose: listing assets, querying production, querying downtime, summarizing throughput, and detecting anomalies. There is no overlap in functionality, making it easy for an agent to select the correct tool for a given task.
All tool names follow a clear verb_noun pattern (e.g., list_assets, query_production). The only minor deviation is 'find_anomalies' instead of 'list_anomalies' or 'query_anomalies', but it still maintains the verb_noun structure and is consistent with the overall style.
With 5 tools, the server is well-scoped for an operational data analysis domain. Each tool provides essential functionality without being excessive, covering asset listing, data querying, summarization, and anomaly detection appropriately.
The tool set covers core querying and analysis needs (production, downtime, throughput, anomalies) but lacks write operations (e.g., adding or updating assets/events) and more advanced analysis (e.g., trend forecasting). This is acceptable for a read-only data exploration server, but leaves gaps for full lifecycle management.