Snippets MCP

MCP server for storing, searching, and managing code snippets using semantic search and traditional keyword matching.
Just tell your coding agent (claude code, cursor, cline, opencode, etc.) to save a certain snippet. That's it.
When needed, just tell it to search for code snippets related to: your query.

Features

Semantic search using AI embeddings to find snippets by meaning, not just keywords
Hybrid search combining semantic similarity and keyword matching
Automatic programming language detection
Tag-based organization and filtering
Date range filtering
No database needed. JSON based storage.
Vector embeddings cached for fast retrieval

Installation

npm install @freakynit/snippets-mcp

Available Tools

add-snippet

Adds a new code snippet to the database.

Parameters:

code (string, required) - The code content
tags (array, optional) - Array of tag strings
language (string, optional) - Programming language (auto-detected if not provided)
description (string, optional) - Text description for better semantic search

search-snippets

Searches snippets using hybrid semantic and keyword matching.

Parameters:

query (string, optional) - Natural language search query
tags (array, optional) - Filter by specific tags (AND logic)
language (string, optional) - Filter by programming language
dateStart (ISO date string, optional) - Filter by creation date start
dateEnd (ISO date string, optional) - Filter by creation date end
limit (number, optional) - Maximum results to return (default: 10)

update-snippet

Updates an existing snippet. Re-generates embeddings if code, tags, or description change.

Parameters:

id (string, required) - Snippet ID
updates (object) - Object containing fields to update (code, tags, language, description)

delete-snippet

Deletes a snippet from the database.

Parameters:

id (string, required) - Snippet ID

get-snippet

Retrieves a single snippet by ID.

Parameters:

id (string, required) - Snippet ID

Environment Variables

SNIPPETS_FILE_PATH: Optional, Full path to file to save snippets and embeddings in. Defaults to ~/.snippets-mcp-db.json.

How It Works

The library uses a hybrid search approach:

Semantic Search (70% weight) - Uses the all-MiniLM-L6-v2 model to perform vector searh against embeddings generated off code, description, tags and language.
Keyword Matching (30% weight) - Traditional text matching for exact term matches based on code and tags.
Hard Filters - Applied first to narrow results by tags, language, and date range.

Embeddings are generated once when adding/updating snippets and cached for fast retrieval.

Storage

Snippets are stored in a JSON file specified by environment variable SNIPPETS_FILE_PATH, or at default path: ~/.snippets-mcp-db.json with the following structure:

{ "id": "uuid", "code": "string", "language": "string", "tags": ["array"], "description": "string", "embedding": [/* vector array */], "createdAt": "ISO date", "updatedAt": "ISO date" }

Configuring using mcpServers json

For Mac and Linux

{ "mcpServers": { "snippets-mcp": { "command": "npx", "args": ["-y", "@freakynit/snippets-mcp@latest"], "env": { "SNIPPETS_FILE_PATH": "Optional... path to save snippets and embeddings in.. should have .json extension" } } } }

For Windows

{ "mcpServers": { "snippets-mcp": { "command": "cmd", "args": ["/k", "npx", "-y", "@freakynit/snippets-mcp@latest"], "env": { "SNIPPETS_FILE_PATH": "Optional... path to save snippets and embeddings in.. should have .json extension" } } } }