MnemoQ
OfficialServer 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
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| retrieve_learningsA | Retrieve relevant learnings for the current task context. Returns warnings (critical issues) and patterns (architectural guidance), scored and ranked by relevance. |
| log_learningA | Log a new learning entry. Validates, checks for duplicates/semantic duplicates, and appends to memory. Returns status (added/duplicate/semantic_duplicate/conflict/quarantined) and entry details. |
| resolve_learningB | Mark an existing learning entry as resolved by its timestamp. |
| get_statsA | Get memory system statistics: total entries, unresolved/resolved counts, severity/type/scope breakdowns, reinforcement patterns, and sleep cycle status. |
| consolidateB | Trigger a Sleep Cycle (consolidation): archives unresolved entries, generates promotion candidates, detects contradictions, and checks for stale entries. |
| evaluate_promptB | Evaluate a structured prompt summary for learnable moments. Runs heuristic detectors on the summary, auto-logs high-confidence signals, and returns suggestions for medium-confidence ones. |
| review_agentsB | Diagnostic report on AGENTS.md section health. Cross-references recent learnings with AGENTS.md sections, categorizing sections as active (referenced by learnings), cold (no references), and identifying unmatched learnings. |
| capture_interactionA | Capture a conversation interaction as memory. Extracts learnable moments from raw text and auto-logs them. Three-tier extraction: online LLM, offline LLM, heuristic fallback. |
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
Each tool has a clearly distinct purpose: capturing raw interactions, logging structured learning, evaluating prompts, retrieving relevant learnings, resolving entries, consolidating memory, and reviewing agent documentation. No two tools overlap in function.
Most tools follow a clear verb_noun pattern in snake_case (e.g., capture_interaction, log_learning, retrieve_learnings). However, 'consolidate' breaks the pattern as a single verb without a noun, causing a minor inconsistency.
With 8 tools, the server covers core memory operations (create, read, update, consolidate, evaluate) without being overwhelming. The count is well-scoped for a memory management system.
The server lacks direct update and delete operations for learning entries, which are common in memory systems. While resolve_learning provides status change, editing entry content is missing. Retrieval is limited to context-based relevance, missing full listing.