Provides integration with GitHub Copilot Chat in VS Code, allowing users to index, search, and manage notes through conversational commands using the MCP server's full-text search and graph capabilities.
Uses Pino for structured, verbose logging with configurable log levels and pretty-printing options for development and debugging.
Uses SQLite with FTS5 for fast local storage and full-text search capabilities across note content, keys, tags, and metadata, with WAL mode for performance and concurrency.
Implemented in TypeScript providing type safety and modern JavaScript features for the MCP server implementation.
Quick Tip: Copy-Paste Text and Images
You can simply copy and paste text or images into your favorite chat client (like Copilot or Anthropic) and say something like:
Add all this to my notes
The LLM will index the information as it decides, using its own context and capabilities. This makes it easy to capture and organize information without manual formatting or tool calls.
MCP Index Notes
A simple, fast MCP server to index and retrieve notes using SQLite (FTS5) with optional JSON backups. Written in TypeScript with verbose logging.
Features
- Fast local storage using better-sqlite3
- Full-text search (FTS5) across content, key, tags, metadata
- Upsert by id or insert by key
- Query by key or text
- Delete by id or key
- Backup to JSON and restore
- Structured, verbose logging via pino
Tools
- index-upsert: Create/update a note
- index-query: Query by key or full-text search
- index-delete: Delete by id or key
- index-backup: Export all notes to JSON
- index-restore: Import notes from JSON
- index-list-keys: Return keys with counts
- index-health: Health check
Graph tools:
- graph-node-upsert: Create/update a graph node
- graph-neighbors: Get neighbors of a node
- graph-path: Find a path between nodes
- graph-import-from-notes: Build graph from existing notes (note->key, note->tags)
- graph-stats: Node/edge counts
Image tools:
- image-upsert: Store an image (base64 or file path) under a key
- image-get: Retrieve images by id or key (optionally include base64 data)
- image-delete: Delete single image by id or all images under a key
- image-export: Export stored images (by id or key) to files on disk
Quick start
- Install deps
- Dev run (stdio MCP server)
- Smoke test (local DB only)
Environment vars:
- DB_PATH: path for SQLite db (default ./data/notes.db)
- LOG_LEVEL: pino level (trace|debug|info|warn|error)
- LOG_PRETTY: true for human-readable logs
Integrations
Below are ready-to-copy examples for popular MCP hosts and clients. Replace paths with your local ones on Windows. All examples run this server via Node and pass optional env vars.
Tip: Build once so the dist entry exists.
GitHub Copilot Chat (VS Code)
Copilot Chat supports MCP servers. Add a new MCP server entry pointing to your built script.
- Open VS Code Settings (JSON) and add an MCP server entry under the Copilot MCP section (exact setting label may vary by version). Use this structure:
Then in Copilot Chat, ask it to call a tool, e.g.: “Call tool index-health”. You should see { "ok": true }
in the result.
Claude Desktop
Add to Claude Desktop’s settings.json (Help → Open config file). Example:
Cursor
Create or edit ~/.cursor/mcp.json
:
Continue.dev
Add to ~/.continue/config.json
under mcpServers
(structure may vary by version):
Verify the connection
Prompt Examples (How to use MCP tools in chat)
Advanced LLM Prompts
You can leverage advanced LLMs to interact with MCP tools for more intelligent workflows. Here are some example prompts:
Semantic Search
Expected result: Returns notes relevant to database security using full-text search.
Summarize Notes
Expected workflow: The client calls index-query
with { tags: "meeting" }
, then uses the LLM to summarize the returned notes.
Generate Knowledge Graph
Expected workflow: The client calls graph-import-from-notes
and graph-stats
to visualize relationships between notes and tags.
Find Shortest Path Between Concepts
Expected result: Returns the path of related notes/tags between the two concepts.
Context-Aware Upsert
Expected workflow: The client upserts the note and updates the graph to connect related concepts.
Multi-step Reasoning
Expected workflow: The client queries notes, summarizes with LLM, and generates actionable suggestions.
You can interact with the MCP server using natural language prompts in your chat client. Here are some example prompts:
Health Check
Expected result: { "ok": true }
Add a Note
Expected result: { "id": ... }
Query a Note by Key
Expected result: Returns stored entries for that key
Full-Text Search
Expected result: Returns notes containing the word "connection"
List All Keys
Expected result: List of all note keys with counts
Delete a Note
Expected result: Confirmation of deletion
Backup Notes to JSON
Expected result: JSON export of all notes
Restore Notes from JSON
Expected result: Notes imported from backup
Graph Tools
Expected result: Graph operations as described
Store an Image (base64)
Returns: { "id": ... }
Store an Image (file path)
Get Images By Key (metadata only)
Get Images Including Data
Returns array with base64 data
field.
Delete Image
Delete All Images Under a Key
Export Images to Files
Returns list of written file paths.
If your client shows a tools list, you should see all tools from this server.
JSON backup format
Notes
- The MCP server communicates via stdio. Integrate with your LLM runtime that supports MCP.
- The DB uses WAL mode for concurrency and performance.
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Tools
Enables indexing and retrieving notes with full-text search using SQLite, plus building knowledge graphs to find relationships between concepts. Supports natural language note management, tagging, and semantic connections.
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