Strata Memory MCP Server
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 | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| strata_initA | First-time initialization with dual-mode setup. Choose 'personal' (CBT safety, 48h cooling) or 'company' (multi-tenancy, AuditLog, private embedding). |
| memorizeB | Record a conversation/fact into memory with psych-validated metadata (emotional_salience, context_tags, is_negative_schema). Writes Markdown drawer + vector index. |
| wake_upC | Session wake-up: L0 profile + L1 diary + L2 semantic search with CBT defusion for negative schemas. Returns flat Markdown. |
| searchB | Active semantic search across L2 memories with time/category/tag filters and state-dependent boosting. |
| get_healthA | Runtime status: initialized, mode, CBT, audit, vector/drawer count, config summary. |
| get_system_profileA | [Agent-Driven] Silent hardware profiling. Returns OS, RAM, CPU cores, GPU accelerator. No user input required — call this first during onboarding. |
| search_embedding_recommendationsA | [Agent-Driven] Return ranked embedding recommendations based on hardware profile. Gets best-fit local/cloud models from MTEB-informed lookup table. |
| apply_memory_configA | [Agent-Driven] Apply chosen memory configuration: persist config, initialize Palace directories, ChromaDB, SQLite. Supports hot reload — no MCP restart needed. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Onboarding Guide | Dual-mode initialization steps (personal + company). |
| Agent-Driven Setup Guide | [Agent-Driven] Dynamic setup instructions for the AI agent. Read this first during onboarding to learn the Agent-Driven initialization flow. |
| Memory Statistics | System stats: drawer/vector count, mode, config. |
| Wing Directory | All wings and their rooms. |
TDQS
Scored across 8 tools
Each tool targets a distinct purpose: initialization, health, profiling, memory writing, searching, recommendations, and session wake-up. No two tools have overlapping functionality that would confuse an agent.
Most tools follow a verb_noun pattern (e.g., apply_memory_config, get_health, search_embedding_recommendations). However, 'strata_init' reverses the order (noun_verb) and 'memorize' is a lone verb without an object, causing slight inconsistency.
With 8 tools, the surface is well-scoped and balanced for a memory management server. Each tool earns its place and covers core workflows without being excessively large or minimal.
The server covers initialization, health, profiling, memorizing, searching, and session wake-up, but lacks update and delete operations for memories. This gap may cause agents to fail when they need to modify or remove stored data.