Which integrations are available for this server?

Provides git versioning for notes, enabling automatic commits, history tracking, and restoration of previous versions. Supports Ollama as an embedding provider for semantic search capabilities. Supports OpenAI-compatible embedding APIs for semantic search across notes and facts.

How do I use mcp-notes?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-notes search notes about quantum computing" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

mcp-notes

by michaelkrauty

Overview Schema Related Servers Score Discussions

Python

Local

mcp-notes

MCP server for personal knowledge management with semantic search, git versioning, and knowledge graph capabilities.

Prerequisites

Python 3.12+
Linux or macOS (uses POSIX file locking via vector-core; not compatible with Windows)
Qdrant vector database (default: localhost:6333)
An OpenAI-compatible embedding API (e.g., llama.cpp, Ollama, or any /v1/embeddings endpoint; default: localhost:8080)
git (must be on PATH for versioning features)

Related MCP server: Notes MCP Server

Installation

Requires vector-core.

pip install git+https://github.com/michaelkrauty/vector-core.git@v1.3.0
pip install git+https://github.com/michaelkrauty/mcp-notes.git

Or clone both repos and install locally:

git clone https://github.com/michaelkrauty/vector-core.git
git clone https://github.com/michaelkrauty/mcp-notes.git
pip install -e vector-core/
pip install -e mcp-notes/

Quick Start

# Register with Claude Code:
claude mcp add notes -- mcp-notes

# Or add to your MCP client config (e.g., claude_desktop_config.json):
# {
#   "mcpServers": {
#     "notes": {
#       "command": "mcp-notes",
#       "env": {
#         "VECTOR_QDRANT_URL": "http://localhost:6333",
#         "VECTOR_EMBEDDING_URL": "http://localhost:8080",
#         "VECTOR_EMBEDDING_MODEL": "your-model-name"
#       }
#     }
#   }
# }

Features

Note Management: CRUD operations on markdown notes with YAML frontmatter
Semantic Search: Hybrid dense + sparse vector search via Qdrant
Git Versioning: Automatic commits on changes, full history with --follow
Wiki Linking: [[uuid]] syntax for inter-note references with backlink tracking
Tags & Categories: Flexible organization with tag management tools
Glossary: Shared term definitions with aliases and domains
Fact Graph: Subject-predicate-object triples with source tracking

Tools (38 total)

Notes (4)

Tool	Description
`create_note`	Create note with auto-generated UUID
`read_note`	Read note by UUID
`update_note`	Update title, content, tags, or category
`delete_note`	Delete note (keeps git history)

Search (3)

Tool	Description
`search_notes`	Hybrid semantic + keyword search with filters
`list_notes`	List notes with tag/category filters
`find_similar_notes`	Find semantically similar notes

Versioning (2)

Tool	Description
`get_note_history`	Git commit history for a note
`restore_note_version`	Restore note to previous commit

Links (1)

Tool	Description
`get_note_links`	Outgoing, incoming (backlinks), and broken links

Tags (3)

Tool	Description
`list_tags`	All tags with note counts
`rename_tag`	Rename tag across all notes
`merge_tags`	Merge multiple tags into one

Categories (2)

Tool	Description
`list_categories`	Category hierarchy with counts
`move_category`	Move/rename a category

Glossary (6)

Tool	Description
`add_glossary_entry`	Add term with expansion, definition, domain
`lookup_term`	Exact lookup by term or alias
`search_glossary`	Semantic glossary search
`list_glossary`	List entries with optional domain filter
`update_glossary_entry`	Modify entry metadata
`delete_glossary_entry`	Delete entry

Facts (11)

Tool	Description
`add_fact`	Add SPO triple with source tracking
`add_facts_batch`	Batch import facts
`update_fact`	Update fact metadata
`delete_fact`	Delete fact and sources
`query_facts`	Query by subject/predicate/object
`get_entity`	All facts involving an entity
`list_facts`	List fact summaries
`search_facts`	Semantic fact search
`index_facts`	Index facts for search
`find_connections`	BFS graph traversal between entities
`get_neighbors`	Immediate entity connections

Integrity (4)

Tool	Description
`get_facts_with_stale_sources`	Facts with deleted/modified sources
`get_source_statistics`	Source integrity stats
`check_fact_integrity`	Check specific fact's sources
`revalidate_fact_sources`	Reset sources after verification

Health (2)

Tool	Description
`reindex_notes`	Force full reindex
`check_note_health`	Validate note structure, find errors

Data Model

Note

---
id: {uuid}
title: Note Title
created: 2024-01-01T00:00:00Z
modified: 2024-01-05T12:00:00Z
tags: [tag1, tag2]
links: [linked-uuid]
---
Markdown content with [[uuid]] links.

Fact (SPO Triple)

subject: "Ada Lovelace" (type: person)
predicate: "works_at"
object: "Babbage Labs" (type: organization)
context: "as lead engineer"
confidence: 1.0
valid_from/to: date range
sources: [{type: note, id: uuid, location: "paragraph 3"}]

Storage

Data	Location
Notes	`{NOTES_DIR}/notes/` (default `~/notes/notes/`)
UUID index	`{NOTES_DIR}/.index/`
Vectors	Qdrant collection `notes_{hash}`
Git repo	`{NOTES_DIR}/.git/`
Facts & glossary	`~/.local/share/vector-core/` (configurable via `VECTOR_SHARED_DATA_DIR`)

NOTES_DIR (default ~/notes) is the base directory. Actual note files live in the notes/ subdirectory within it. The base directory also contains .index/, .git/, and .locks/.

Notes stored as {slug}-{uuid}.md organized by category subdirectories.

Configuration

Variable	Default	Description
`NOTES_DIR`	`~/notes`	Notes storage directory
`NOTES_GIT_ENABLED`	`true`	Enable git versioning
`NOTES_GIT_USER_NAME`	`Notes MCP`	Git commit author
`NOTES_GIT_USER_EMAIL`	`notes@localhost`	Git commit email
`NOTES_AUTO_INDEX`	`true`	Auto-index on startup
`NOTES_COLLECTION_PREFIX`	`notes`	Qdrant collection prefix

Plus inherited vector-core settings (VECTOR_QDRANT_URL, VECTOR_EMBEDDING_URL, etc.).

Search Query Syntax

# Filter by tag
tag:project-x

# Exclude a tag
-tag:archived

# Filter by category (exact match)
category:work/projects

# Date filters
after:2024-01-01
before:2024-06-30

# Title search
title:meeting notes

# Combined
project tag:active category:work after:2024-01-01

MCP Resources

Static data endpoints:

notes://index - Full note index
notes://tags - All tags with counts
notes://categories - Category hierarchy
notes://recent - Last 20 modified notes
notes://orphans - Notes with no backlinks
notes://broken-links - Broken reference summary
notes://parse-errors - Notes that failed to parse

Dependencies

Requires vector-core components:

EmbeddingClient, GlobalVocabulary (search)
QdrantStorage (storage)
GlossaryStore, FactStore (knowledge graph)

External libraries:

GitPython (versioning)

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

<1hResponse time

1dRelease cycle

2Releases (12mo)

Commit activity

Issues opened vs closed

Resources

GitHub Repository

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