Required for running the Weaviate vector database option, with included scripts for managing the Docker-based Weaviate environment.
Provides access to Git-related information, including commit message formats and conventions through the knowledge base search functionality.
Enables indexing and searching of Markdown (.md, .mdx) files, allowing AI assistants to retrieve information from documentation stored in Markdown format.
Supports Milvus as one of the vector store backends for storing and retrieving document embeddings in the knowledge base.
Uses OpenAI's API for vectorizing documents to create embeddings for the knowledge base, requiring an API key for operation.
Implements type-safe interfaces for search requests and results, enabling structured interaction with the knowledge base through strongly-typed APIs.
This is a knowledge base MCP server that can be used in common with various AI assistants (CLINE, Cursor, Windsurf, Claude Desktop). It utilizes Retrieval Augmented Generation (RAG) to realize efficient information search and utilization. By sharing the knowledge base between multiple AI assistant tools, it provides consistent information access.
A common knowledge base can be used across multiple AI assistants
High-precision information retrieval using RAG
Type-safe implementation using TypeScript
Supports multiple vector stores (HNSWLib, Chroma, Pinecone, Milvus)
Extensibility through abstracted interfaces
Related MCP server: Confluence MCP Server
git clone https://github.com/yourusername/shared-knowledge-mcp.git
cd shared-knowledge-mcp
npm installThe MCP server settings are added to the configuration file of each AI assistant.
~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json :
{
"mcpServers": {
"shared-knowledge-base": {
"command": "node",
"args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
"env": {
"KNOWLEDGE_BASE_PATH": "/path/to/your/rules",
"OPENAI_API_KEY": "your-openai-api-key",
"SIMILARITY_THRESHOLD": "0.7",
"CHUNK_SIZE": "1000",
"CHUNK_OVERLAP": "200",
"VECTOR_STORE_TYPE": "hnswlib"
}
}
}
}{
"mcpServers": {
"shared-knowledge-base": {
"command": "node",
"args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
"env": {
"KNOWLEDGE_BASE_PATH": "/path/to/your/rules",
"OPENAI_API_KEY": "your-openai-api-key",
"VECTOR_STORE_TYPE": "pinecone",
"VECTOR_STORE_CONFIG": "{\"apiKey\":\"your-pinecone-api-key\",\"environment\":\"your-environment\",\"index\":\"your-index-name\"}"
}
}
}
}~/Library/Application Support/Claude/claude_desktop_config.json :
{
"mcpServers": {
"shared-knowledge-base": {
"command": "node",
"args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
"env": {
"KNOWLEDGE_BASE_PATH": "/path/to/your/docs",
"OPENAI_API_KEY": "your-openai-api-key",
"SIMILARITY_THRESHOLD": "0.7",
"CHUNK_SIZE": "1000",
"CHUNK_OVERLAP": "200",
"VECTOR_STORE_TYPE": "hnswlib",
"VECTOR_STORE_CONFIG": "{}"
},
"disabled": false,
"autoApprove": []
}
}
}{
"mcpServers": {
"shared-knowledge-base": {
"command": "node",
"args": ["/path/to/shared-knowledge-mcp/dist/index.js"],
"env": {
"KNOWLEDGE_BASE_PATH": "/path/to/your/docs",
"OPENAI_API_KEY": "your-openai-api-key",
"SIMILARITY_THRESHOLD": "0.7",
"CHUNK_SIZE": "1000",
"CHUNK_OVERLAP": "200",
"VECTOR_STORE_TYPE": "weaviate",
"VECTOR_STORE_CONFIG": "{\"url\":\"http://localhost:8080\",\"className\":\"Document\",\"textKey\":\"content\"}"
},
"disabled": false,
"autoApprove": []
}
}
}Note : If you are using Weaviate, you must first start the Weaviate server, which can be done with the following command:
./start-weaviate.shnpm run devnpm run buildnpm startSearch for information in the knowledge base.
interface SearchRequest {
// 検索クエリ（必須）
query: string;
// 返す結果の最大数（デフォルト: 5）
limit?: number;
// 検索のコンテキスト（オプション）
context?: string;
// フィルタリングオプション（オプション）
filter?: {
// ドキュメントの種類でフィルタリング（例: ["markdown", "code"]）
documentTypes?: string[];
// ソースパスのパターンでフィルタリング（例: "*.md"）
sourcePattern?: string;
};
// 結果に含める情報（オプション）
include?: {
metadata?: boolean; // メタデータを含める
summary?: boolean; // 要約を生成
keywords?: boolean; // キーワードを抽出
relevance?: boolean; // 関連性の説明を生成
};
}Basic search:
const result = await callTool("rag_search", {
query: "コミットメッセージのフォーマット",
limit: 3
});Advanced Search:
const result = await callTool("rag_search", {
query: "コミットメッセージのフォーマット",
context: "Gitの使い方について調査中",
filter: {
documentTypes: ["markdown"],
sourcePattern: "git-*.md"
},
include: {
summary: true,
keywords: true,
relevance: true
}
});interface SearchResult {
// 検索クエリに関連する文書の内容
content: string;
// 類似度スコア（0-1）
score: number;
// ソースファイルのパス
source: string;
// 位置情報
startLine?: number; // 開始行
endLine?: number; // 終了行
startColumn?: number; // 開始桁
endColumn?: number; // 終了桁
// ドキュメントの種類（例: "markdown", "code", "text"）
documentType?: string;
// 追加情報（include オプションで指定した場合のみ）
summary?: string; // コンテンツの要約
keywords?: string[]; // 関連キーワード
relevance?: string; // 関連性の説明
metadata?: Record<string, unknown>; // メタデータ
}{
"results": [
{
"content": "# コミットメッセージのフォーマット\n\n以下の形式でコミットメッセージを記述してください：\n\n```\n<type>(<scope>): <subject>\n\n<body>\n\n<footer>\n```\n\n...",
"score": 0.92,
"source": "/path/to/rules/git-conventions.md",
"startLine": 1,
"endLine": 10,
"startColumn": 1,
"endColumn": 35,
"documentType": "markdown",
"summary": "コミットメッセージのフォーマットについての説明文書",
"keywords": ["commit", "message", "format", "type", "scope"],
"relevance": "このドキュメントは検索クエリ \"コミットメッセージのフォーマット\" に関連する情報を含んでいます。類似度スコア: 0.92"
}
]
}These expanded search capabilities enable LLM to process information more accurately and efficiently. Additional information such as location, document type, abstract, and keywords help LLM to better understand and utilize search results.
At startup, it reads Markdown files (.md, .mdx) and text files (.txt) in the specified directory.
Split the document into chunks and vectorize it using the OpenAI API
Creates a vector index using the selected vector store (default: HNSWLib)
Returns documents that are highly similar to a search query
HNSWLib : A fast vector store stored on the local file system (default)
Chroma : an open source vector database
Pinecone : Managed vector database service (API key required)
Milvus : A large-scale vector search engine
Weaviate : A schema-first vector database (Docker required)
Each vector store is exposed through an abstracted interface, making it easy to switch between them as needed.
HNSWLib saves the vector store on the local file system, so no special configuration is required.
Vector store reconstruction:
./rebuild-vector-store-hnsw.shTo use Weaviate, you need Docker.
Start the Weaviate environment:
./start-weaviate.shVector store reconstruction:
./rebuild-vector-store-weaviate.shCheck the status of Weaviate:
curl http://localhost:8080/v1/.well-known/readyStopping the Weaviate environment:
docker-compose downDelete your Weaviate data completely (only if necessary):
docker-compose down -vWeaviate configuration is managed in the docker-compose.yml file. By default, the following settings are applied:
Port: 8080
Authentication: Anonymous access enabled
Vectorization module: None (use external padding)
Data storage: Docker volume ( weaviate_data )
environmental variables | explanation | Default value |
KNOWLEDGE_BASE_PATH | Knowledge Base Path (required) | - |
OPENAI_API_KEY | OpenAI API key (required) | - |
SIMILARITY_THRESHOLD | Similarity score threshold for search (0-1) | 0.7 |
CHUNK_SIZE | Chunk size for splitting text | 1000 |
CHUNK_OVERLAP | Chunk overlap size | 200 |
VECTOR_STORE_TYPE | The type of vector store to use ("hnswlib", "chroma", "pinecone", "milvus"). | "hnswlib" |
VECTOR_STORE_CONFIG | Vector store configuration (JSON string) | {} |
ISC
Fork
Create a feature branch ( git checkout -b feature/amazing-feature )
Commit the changes ( git commit -m 'Add some amazing feature' )
Push to the branch ( git push origin feature/amazing-feature )
Create a Pull Request
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Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
Connect your team's living knowledge base — docs, data, issues, CRM — to Claude and ChatGPT.
A personal RAG database you build from chat, so AI creates work that sounds like you.
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