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MCP Tools
SuperLocalMemory exposes 32 tools and 7 resources via the Model Context Protocol (MCP). These are what your IDE uses to interact with the memory system.
v3.6.11 New: 5 Optimize tools —
slm_compress,slm_retrieve,slm_cache_set,slm_cache_get,slm_optimize_stats. Proxy-free compression + routed-result caching. Full 1M context window preserved.
V3.1 New: 3 Active Memory tools (
session_init,observe,report_feedback) and 1 resource (slm://context) for automatic learning and context injection.
slm mcp # Starts stdio transport — your IDE calls this automaticallyYour IDE config should look like:
{
"mcpServers": {
"superlocalmemory": {
"command": "slm",
"args": ["mcp"]
}
}
}| Tool | Parameters | Description |
|---|---|---|
remember |
content, tags?
|
Store a new memory |
recall |
query, limit?
|
Retrieve relevant memories |
search |
query, limit?
|
Search across all memories |
forget |
query |
Delete matching memories |
fetch |
id |
Get a specific memory by ID |
list_recent |
limit? |
List recent memories |
get_status |
— | System status (mode, DB, count, math health) |
health |
— | Math layer health (Fisher, Sheaf, Langevin) |
build_graph |
— | Rebuild the knowledge graph |
get_attribution |
memory_id |
Get provenance chain for a memory |
compact_memories |
— | Compress and optimize storage |
memory_used |
— | Storage usage statistics |
backup_status |
— | Backup and database health |
audit_trail |
limit? |
Recent operations log |
| Tool | Parameters | Description |
|---|---|---|
session_init |
project_path?, query?
|
Auto-recall project context at session start. Returns relevant memories + learning status. Call once at the beginning of every session. |
observe |
content |
Send conversation content for auto-capture. Detects decisions, bug fixes, and preferences. Stores automatically when confidence > 0.5. |
report_feedback |
fact_id, feedback, query?
|
Report whether a recalled memory was useful. Feedback: "relevant", "irrelevant", or "partial". Trains the adaptive ranker. |
| Tool | Parameters | Description |
|---|---|---|
switch_profile |
name |
Switch to a different memory profile |
set_retention_policy |
days, categories?
|
Set data retention period |
report_outcome |
memory_id, outcome
|
Report whether a recalled memory was helpful |
correct_pattern |
pattern_id, correction
|
Correct a learned behavioral pattern |
get_behavioral_patterns |
limit? |
View learned patterns |
get_learned_patterns |
limit? |
View ML-learned recall patterns |
| Tool | Parameters | Description |
|---|---|---|
recall_trace |
query |
Recall with per-channel score breakdown |
get_lifecycle_status |
— | Memory lifecycle health (active/warm/cold counts) |
consistency_check |
— | Run sheaf consistency verification |
set_mode |
mode |
Switch operating mode (a/b/c) |
get_mode |
— | Current operating mode |
MCP resources provide read-only data streams that IDEs can subscribe to.
| Resource | URI | Description |
|---|---|---|
| Memory Stats | memory://stats |
Total memories, storage size, profile count |
| Recent Memories | memory://recent |
Last 10 memories stored |
| Active Profile | memory://profile |
Current profile name and settings |
| System Health | memory://health |
Database status, math layer scores |
| Knowledge Graph | memory://graph |
Graph summary (nodes, edges, communities) |
| Learning State | memory://learning |
ML model state and learned patterns |
Proxy-free compression and routed-result caching. All five are fail-open — any internal error returns ok:False with the original content unchanged; the agent always continues.
| Tool | Parameters | Description |
|---|---|---|
slm_compress |
content, mode?, reversible?, ttl_seconds?
|
Compress text. mode: normalize (lossless), auto, aggressive. Returns ccr_id when lossy+reversible. |
slm_retrieve |
ccr_id |
Recover exact original from a lossy compress. |
slm_cache_set |
key, value, ttl_seconds?
|
Cache any string result (file read, bash output, search). Namespaced per agent. |
slm_cache_get |
key |
Retrieve cached result. Returns hit:True/False. |
slm_optimize_stats |
— | Compression + cache statistics for the current session. |
Hard constraint: Surfaces B and C cache results you explicitly route through SLM — not the Claude conversation turn. Full-turn caching requires Surface A (proxy).
- Your IDE connects to the SuperLocalMemory MCP server via
slm mcp - When you chat with your AI, the IDE calls
recallwith relevant context - SuperLocalMemory runs 4-channel retrieval and returns matching memories
- The IDE injects those memories into the AI's context
- Your AI responds with knowledge of your past work
This happens automatically — you do not need to manually call tools.
See IDE Setup for per-IDE configuration paths.
Part of Qualixar | Created by Varun Pratap Bhardwaj
SuperLocalMemory V3
Getting Started
Reference
Architecture
Enterprise
V2 Documentation