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MCP Server - Vector Search

@omarguzmanm

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

omarguzmanm

关于 MCP Server - Vector Search

MCP Server to improve LLM context through vector search.

#neo4j#mcp#knowledge-graph#llm

MCP Server - Vector Search 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-vector-search": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

MCP Server - Vector Search 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。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is 🔍 MCP Server - Vector Search?

A Model Context Protocol server built with FastMCP that combines Neo4j’s graph database with vector search using embeddings. It enables intelligent semantic search across a knowledge graph through natural language queries, designed for MCP clients such as Claude AI.

How to use 🔍 MCP Server - Vector Search?

After cloning the repository, create a virtual environment with uv, install dependencies (fastmcp, neo4j, openai, python-dotenv, sentence-transformers, pydantic), configure a .env file with Neo4j credentials and an optional OpenAI API key, and create a vector index in Neo4j. Launch the server with python main.py. The server exposes one tool: vector_search_neo4j(prompt), which converts a natural language query into an embedding and searches the vector index for semantically similar nodes.

Key features of 🔍 MCP Server - Vector Search

  • Converts natural language queries into 1536‑dimensional embeddings via OpenAI.
  • Searches a Neo4j vector index for semantically similar nodes.
  • Returns ranked results with similarity scores.
  • Built on FastMCP for minimal overhead and MCP protocol compliance.
  • Uses uv for 10–100x faster dependency resolution.
  • Supports fallback to a local all-MiniLM-L6-v2 embedding model.

Use cases of 🔍 MCP Server - Vector Search

  • Semantic document retrieval from a Neo4j knowledge graph.
  • Finding contextually relevant graph-connected information using plain language.
  • Integrating intelligent search into MCP‑compatible AI assistants (e.g., Claude Desktop).
  • Building RAG‑style applications that combine graph traversal with vector similarity.

常见问题

1

What are the runtime requirements?

Python 3.8+, Neo4j 5.0+ with the APOC plugin, and either an OpenAI API key (for the default 1536‑dimension embeddings) or the `sentence-transformers` library for a local fallback model.

2

Where does the data live?

All data – nodes, their `embedding` properties, and the vector index – is stored inside a Neo4j database. The server only reads from and writes to that database.

3

Is an OpenAI API key required?

No. If `OPENAI_API_KEY` is not set in `.env`, the server falls back to the local `all-MiniLM-L6-v2` model from `sentence-transformers`.

4

What vector index must exist in Neo4j?

A vector index named `embeddableIndex` on nodes labeled `Document` with property `embedding`, dimension 1536, and cosine similarity. If using the local fallback model, adjust the dimension accordingly.

5

How do I troubleshoot a missing vector index?

Use Cypher `SHOW INDEXES` to verify existence, and re‑create it with the `CREATE VECTOR INDEX` command shown in the Quick Start.

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