Brain Server - MCP Knowledge Embedding Service

@patrickdeluca

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Basic information

About Brain Server - MCP Knowledge Embedding Service

No overview available yet

#mcp#knowledge-embedding#vector-search

Brain Server - MCP Knowledge Embedding Service config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

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Brain Server - MCP Knowledge Embedding Service tools

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Overview

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

Frequently asked questions

What is the default embedding model?

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

What database does the server require?

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

How do I configure the server?

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

How do I run the server in development mode?

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

What MCP tools does the server expose?

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

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