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Waggle MCP Server

@Abhigyan-Shekhar

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

Abhigyan-Shekhar

提交者

abhigyan sinha

关于 Waggle MCP Server

Here is a description you can use:

Waggle MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "waggle-mcp": {
      "command": "uvx",
      "args": [
        "waggle-mcp"
      ],
      "env": {
        "WAGGLE_BACKEND": "sqlite",
        "WAGGLE_DB_PATH": "~/.waggle/memory.db"
      }
    }
  }
}

Waggle MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Waggle Mcp?

Waggle Mcp gives AI agents persistent graph memory across sessions. It automatically extracts, deduplicates, and stores facts as typed graph nodes with semantic embeddings, running fully locally without an API key.

How to use Waggle Mcp?

Set up with the single command waggle-mcp init. Then drop conversation turns into Waggle Mcp; facts are automatically extracted and stored for later retrieval.

Key features of Waggle Mcp

  • Persistent graph memory across sessions
  • Automatic fact extraction and deduplication
  • Typed graph nodes with semantic embeddings
  • Temporal query support ("what did we decide last week?")
  • Conflict detection and context priming
  • Fully local, no API key required

Use cases of Waggle Mcp

  • Maintain coherent long-term memory for AI assistants
  • Retrieve past decisions or facts via temporal queries
  • Provide context priming for ongoing conversation turns
  • Detect and resolve conflicting facts across sessions

常见问题

1

Does Waggle Mcp require an API key?

No, Waggle Mcp runs fully locally and does not require any API key.

2

How is Waggle Mcp set up?

It uses a one-command setup: run `waggle-mcp init` to get started.

3

Can Waggle Mcp answer temporal queries?

Yes, it supports temporal queries such as "what did we decide last week?".

4

What happens to duplicate facts?

Facts are automatically deduplicated during storage.

5

What type of memory does Waggle Mcp use?

It uses persistent graph memory with typed nodes and semantic embeddings.

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