MCP Standards
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | No | Your Gemini API key for cost optimization routing simple operations to cheaper models |
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 |
|---|---|
| add_episodeC | Add knowledge episode to memory |
| search_episodesC | Search knowledge episodes |
| list_recentC | List recent episodes |
| log_tool_executionC | Log tool execution for learning |
| export_to_markdownC | Export knowledge base to markdown files |
| generate_ai_standardsB | Auto-generate AI assistant instruction files (CLAUDE.md, .github/copilot-instructions.md, .cursor/rules/standards.mdc) from existing project config files (.editorconfig, .prettierrc, ESLint, pyproject.toml, etc.) |
| get_learned_preferencesB | Get all learned preferences with confidence scores (automatically learned from corrections) |
| suggest_claudemd_updateA | Get suggestions for CLAUDE.md updates based on learned patterns (does not apply them) |
| update_claudemdB | Update CLAUDE.md file with learned preferences (creates backup first) |
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 9 tools
Most tools have distinct purposes, but some potential overlap exists between 'suggest_claudemd_update' and 'update_claudemd' where an agent might need to understand the difference between suggestion and application. The core memory operations (add_episode, list_recent, search_episodes) are clearly differentiated, and export/learning tools serve separate functions.
All tools follow a consistent verb_noun naming pattern with snake_case throughout. The naming convention is predictable and readable, with clear action-object relationships (e.g., add_episode, search_episodes, update_claudemd). No mixed conventions or style deviations are present.
With 9 tools, the count is well-scoped for a knowledge/standards management server. Each tool appears to serve a specific purpose in the workflow (memory management, export, learning, standards generation), and none seem redundant or unnecessary given the domain.
The tool set covers core workflows for knowledge management (add, list, search), learning from corrections, and standards generation/updating. A minor gap exists in direct memory modification beyond adding episodes (e.g., no update/delete episode tools), but agents can likely work around this through the learning system. The export and auto-generation tools provide good coverage for the stated purpose.