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Brain Server - MCP Knowledge Embedding Service

@patrickdeluca

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

patrickdeluca

关于 Brain Server - MCP Knowledge Embedding Service

暂无概览

#mcp#knowledge-embedding#vector-search

Brain Server - MCP Knowledge Embedding Service 配置

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该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

Brain Server - MCP Knowledge Embedding Service 工具

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概览

What is Brain Server - MCP Knowledge Embedding Service?

Brain Server - MCP Knowledge Embedding Service is an MCP (Model Context Protocol) server for managing knowledge embeddings and vector search. It integrates with AI applications via the Model Context Protocol and organizes knowledge into domain-specific brains.

How to use Brain Server - MCP Knowledge Embedding Service?

Clone the repository, install dependencies (npm install), copy .env.example to .env and configure PORT, MONGODB_URI, EMBEDDING_MODEL, and MAX_CHUNK_SIZE. Build with npm run build, then run in development mode (npm run dev) or production mode (npm start). Use the exposed MCP tools such as addKnowledge, searchSimilar, updateKnowledge, deleteKnowledge, batchAddKnowledge, and getEmbedding.

Key features of Brain Server - MCP Knowledge Embedding Service

  • Generate high-quality vector embeddings for knowledge content
  • Perform semantic search based on meaning, not keywords
  • Organize knowledge into domain-specific brains
  • Retrieve surrounding context for better understanding
  • Track progress of long-running operations in real time
  • Full compliance with the Model Context Protocol

Use cases of Brain Server - MCP Knowledge Embedding Service

  • Store and manage knowledge embeddings for AI assistant context
  • Search large knowledge bases by semantic similarity
  • Batch add multiple knowledge entries for bulk ingestion
  • Organize knowledge into separate brains for different domains
  • Monitor embedding and ingestion progress during processing

常见问题

1

What is the default embedding model?

The default embedding model is `Xenova/all-MiniLM-L6-v2`, configured via the `EMBEDDING_MODEL` environment variable.

2

What database does the server require?

The server requires MongoDB, configured through the `MONGODB_URI` environment variable.

3

How do I configure the server?

Copy `.env.example` to `.env` and set `PORT`, `MONGODB_URI`, `EMBEDDING_MODEL`, and `MAX_CHUNK_SIZE` as needed.

4

How do I run the server in development mode?

Run `npm run dev` for development mode with hot reloading.

5

What MCP tools does the server expose?

The server exposes `addKnowledge`, `searchSimilar`, `updateKnowledge`, `deleteKnowledge`, `batchAddKnowledge`, and `getEmbedding`.

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