MCP Self-Learning Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_FILE | No | Enable file logging | true |
| NODE_ENV | No | Environment mode | production |
| LOG_LEVEL | No | Logging level (debug/info/warn/error) | info |
| LOG_CONSOLE | No | Enable console logging | true |
| LEARNING_MODE | No | Learning behavior mode | autonomous |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_patternC | Analyze and learn from interaction patterns |
| get_insightsC | Get learning insights and recommendations |
| trigger_learningC | Manually trigger a learning cycle |
| export_knowledgeC | Export current knowledge base |
| import_knowledgeD | Import external knowledge |
| optimize_toolC | Get optimization suggestions for a specific tool |
| predict_next_actionD | Predict the next likely action based on context |
| get_performance_metricsC | Get detailed performance metrics |
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 8 tools
Most tools have distinct purposes, such as analyze_pattern for pattern analysis, export_knowledge for exporting, and get_insights for insights. However, get_insights and get_performance_metrics could be slightly overlapping, as insights might include performance data, but their descriptions help differentiate them.
All tool names follow a consistent verb_noun pattern with clear, descriptive verbs like analyze, export, get, import, optimize, predict, and trigger. The naming is uniform and predictable throughout the set.
With 8 tools, the count is well-scoped for a self-learning server, covering key operations like analysis, knowledge management, insights, and optimization. Each tool appears to serve a specific function without redundancy.
The tool set covers core self-learning functions, including analysis, knowledge import/export, insights, metrics, optimization, prediction, and manual triggering. A minor gap might be the lack of a tool for resetting or clearing knowledge, but overall, it supports the server's purpose effectively.