Memory Arbiter Mcp
@billy12151
Memory Arbiter Mcp について
Share one memory store across all your AI coding tools. Memory Arbiter is a lightweight, fully local MCP Server with built-in dual-timeline conflict arbitration. No cloud, no LLM calls — just SQLite.
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"memory-arbiter": {
"command": "memory-arbiter-mcp",
"env": {
"MEMORY_ARBITER_CLIENT": "zcode",
"MEMORY_ARBITER_AGENT_ID": "zcode-default",
"MEMORY_ARBITER_DB_PATH": "~/.local/share/memory-arbiter/memory.sqlite3"
}
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Memory Arbiter MCP?
Memory Arbiter MCP is a Model Context Protocol server that provides a shared local memory store, backed by SQLite, for multiple AI coding tools such as ZCode, Codex, Cursor, and Claude Code. It is designed for developers who need a consistent memory layer across different assistants without relying on cloud services.
How to use Memory Arbiter MCP?
Install the package with pip install memory-arbiter-mcp, then add it as an MCP server to each AI coding tool you use. The shared memory database is stored locally in an SQLite file; no additional infrastructure is required.
Key features of Memory Arbiter MCP
- Shared memory store across multiple AI coding tools
- Conflict arbitration with multi-factor resolution
- User-confirmed memories locked from agent overwrites
- Zero cloud dependencies, all local SQLite
- Graceful degradation through fallback search methods
Use cases of Memory Arbiter MCP
- Maintain a consistent knowledge base across different coding assistants
- Preserve user-confirmed context that agents cannot accidentally overwrite
- Resolve conflicting memory entries automatically during multi-agent workflows
- Keep all memory data locally for privacy and offline usage
- Fall back to simpler search methods when vector search is unavailable
FAQ from Memory Arbiter MCP
How does conflict arbitration work?
Conflicts are resolved using a dual-timeline priority: user-confirmed entries take precedence, then event time, source trust level, and finally ingest time.
Can agents overwrite memories I’ve confirmed?
No. User-confirmed memories are locked and cannot be overwritten by agents, ensuring critical context remains stable.
Does this server require cloud services or LLM calls?
No. Everything runs locally on SQLite with no cloud dependencies and zero LLM calls for memory operations.
What happens if sqlite-vec is not installed?
The server degrades gracefully through fallbacks: first FTS5, then LIKE queries, and finally reading from a JSONL backup.
How is the memory store shared between tools?
Each tool connects to the same MCP server, which reads and writes a single local SQLite database file.
「メモリとナレッジ」の他のコンテンツ
Rust Docs MCP Server
Govcraft🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.
MemoryMesh
CheMiguel23A knowledge graph server that uses the Model Context Protocol (MCP) to provide structured memory persistence for AI models.
Notion MCP Server
suekouA Model Context Protocol server for connecting Notion to MCP-compatible clients
Context Portal MCP (ConPort)
GreatScottyMacContext Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.
MCP Apple Notes
RafalWilinskiTalk with your notes in Claude. RAG over your Apple Notes using Model Context Protocol.
コメント