MCP Tools

MCP Tools

SuperLocalMemory exposes 27 tools and 7 resources via the Model Context Protocol (MCP). These are what your IDE uses to interact with the memory system.

V3.1 New: 3 Active Memory tools (session_init, observe, report_feedback) and 1 resource (slm://context) for automatic learning and context injection.

Starting the MCP Server

slm mcp    # Starts stdio transport — your IDE calls this automatically

Your IDE config should look like:

{
  "mcpServers": {
    "superlocalmemory": {
      "command": "slm",
      "args": ["mcp"]
    }
  }
}

Core Tools

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

Active Memory Tools (V3.1)

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.

Management Tools

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

V3 Tools

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

Resources (6)

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

How MCP Integration Works

1. Your IDE connects to the SuperLocalMemory MCP server via slm mcp
2. When you chat with your AI, the IDE calls recall with relevant context
3. SuperLocalMemory runs 4-channel retrieval and returns matching memories
4. The IDE injects those memories into the AI's context
5. 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.

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Part of Qualixar | Created by Varun Pratap Bhardwaj