Self-Learning MCP Server

Self-improving memory for AI agents — Antigravity-native MCP server

A persistent memory system that lets AI agents learn from their own work. Records tasks, extracts patterns, detects mistakes, and proactively surfaces insights — all using the agent's own model through a cooperative intelligence pattern.

Quick Start (Antigravity)

# 1. Clone and build
git clone <repo-url> && cd Self-Learning-MCP
npm install && npm run build

# 2. Register in Antigravity
node dist/src/cli.js init

That's it. No API keys. No model config. No env vars. The server uses Antigravity's own model for all reasoning.

How It Works

Agent-Cooperative Intelligence

Unlike traditional memory systems that need their own LLM, this server uses a cooperative pattern:

1. Server handles storage, retrieval, and structuring (SQLite + FTS5)
2. Agent (running on Antigravity's model) does all reasoning and synthesis
3. Agent commits learned patterns back to the server

Agent does work → calls mem_end_task → server returns synthesis context
→ agent reasons over it → calls mem_commit_synthesis → patterns stored
→ next task: mem_get_briefing → patterns influence approach

The Learning Loop

┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐
│  Record  │────▶│  Recall  │────▶│Synthesize│────▶│ Proactive│
│          │     │          │     │          │     │          │
│ Tasks    │     │ Briefings│     │ Patterns │     │ Insights │
│ Steps    │     │ Context  │     │ Anti-pat │     │ Drift    │
│ Errors   │     │ Wiki     │     │ Wiki     │     │ Risks    │
└──────────┘     └──────────┘     └──────────┘     └──────────┘

Compact Wire Codec

All tool outputs use a token-efficient format (~77% smaller than verbose JSON):

Verbose: {"type":"pattern","description":"validate webhooks","confidence":0.95,"tags":["api","security"]}
Compact: {"t":"P","d":"validate webhooks","c":95,"ta":"api|security"}

Tools Reference

Record (6 tools)

Tool	Description
mem_start_task	Begin a task trace {d, j?, conv?} → {tid, has_insights}
mem_record_step	Record a step {tid, action, tool?, result?, ok?}
mem_record_correction	Record a correction {tid, wrong, fix, cause?}
mem_end_task	Close trace {tid, outcome?, summary?} → synthesis context
mem_store_entity	Store entity {t, nm, d?, j?, obs?[]}
mem_add_relationship	Create edge {src, tgt, rel}

Recall (6 tools)

Tool	Description
mem_recall	Full-text search {q, n?, t?, j?}
mem_get_context	Context packet for topic {topic, j?}
mem_get_wiki	Retrieve wiki {j?, sec?}
mem_query_graph	Structured query {t?, rel?, j?, since?, n?}
mem_get_entity	Entity details {id}
mem_get_briefing	Pre-task intelligence {d, j?}

Cooperative (3 tools)

Tool	Description
mem_commit_synthesis	Commit learned patterns {tid, patterns[], anti[]}
mem_commit_wiki	Save wiki sections {sections[{sec, j?, x}]}
mem_regenerate_wiki	Gather wiki context {j?}

Proactive (3 tools)

Tool	Description
mem_get_insights	Active insights {t?, n?}
mem_dismiss_insight	Dismiss insight {id, reason?}
mem_set_watch	Set watch {condition, entity_id?}

Compact Codec Decoder Ring

Short Key	Full Name
t	type
d	description
c	confidence (0-100)
h	hit count
j	project
nm	name
x	content
n	count / total
tid	task ID
ok	success
ts	timestamp
sec	section

Entity Types: T=task, P=pattern, E=error, S=solution, J=project, C=code, R=person

Relationships: RB=resolved_by, DP=depends_on, CB=caused_by, IB=improved_by, FB=followed_by, TF=transferred_from, EF=extracted_from, UI=used_in

Configuration

All optional, via environment variables:

Variable	Default	Description
SELF_LEARNING_MCP_DB	~/.gemini/antigravity/self-learning-mcp/memory.db	Database path
SELF_LEARNING_MCP_PROACTIVE_MIN	30	Minutes between proactive analysis
SELF_LEARNING_MCP_STALENESS_DAYS	30	Days before pattern flagged stale
SELF_LEARNING_MCP_CODEC	compact	Wire format: compact or verbose
SELF_LEARNING_MCP_LOG	info	Log level

Architecture

src/
├── server.ts              # MCP entry point + proactive engine startup
├── config.ts              # Env-var configuration
├── cli.ts                 # Init command for Antigravity setup
├── codec/                 # Token-efficient wire format
│   ├── types.ts           # Type codes, field maps
│   ├── encoder.ts         # Internal → compact
│   ├── decoder.ts         # Compact/verbose → internal
│   └── index.ts           # Public API
├── db/
│   ├── schema.sql         # SQLite schema (13 tables + 5 FTS5)
│   └── database.ts        # Database class (SQL embedded)
├── tools/
│   ├── record.ts          # 6 recording tools
│   ├── recall.ts          # 6 recall tools
│   ├── cooperative.ts     # 3 synthesis tools
│   └── proactive.ts       # 3 proactive tools
├── wiki/
│   └── generator.ts       # Wiki context gathering
└── proactive/
    ├── engine.ts           # Hybrid scheduler orchestrator
    ├── staleness-detector.ts
    ├── drift-detector.ts
    ├── risk-forecaster.ts
    ├── opportunity-surfacer.ts
    ├── briefing-assembler.ts
    └── index.ts

Generic MCP Usage

Works with any MCP client, not just Antigravity. Add to your MCP config:

{
  "mcpServers": {
    "self-learning-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/Self-Learning-MCP/dist/src/server.js"]
    }
  }
}

The difference: without instructions.md, the client agent needs to know when to call the memory tools on its own.

License

MIT