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Shaktisinhchavda

memory-mcp

by Shaktisinhchavda
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Python
Local

Personal MCP Ecosystem

A modular, local-first infrastructure that exposes your personal data — files, notes, browser history, code activity, conversations — as unified semantic context via the Model Context Protocol (MCP).

Any AI agent can plug into this and instantly know you.

Python MCP Neo4j Docker License

Features

Capability

Description

Semantic Search

Query your notes by meaning using ChromaDB + sentence-transformers

Knowledge Graph

Neo4j-backed entity extraction with relationship mapping

Memory Write-back

AI agents can save new notes and append to existing ones

File Reader

Read PDFs, DOCX, Markdown, code files from anywhere on disk

Browser History

Search Chrome, Edge, and Firefox history (auto-detected)

Code Activity

Git commits, repo stats, and VSCode recent files

Conversations

Parse exported Claude & ChatGPT conversation JSON files

Calendar

Google Calendar integration via OAuth 2.0 (requires setup)

Event Logger

Real-time file change tracking via watchdog

Unified Gateway

FastAPI + LangGraph agent that routes queries across all sources

Incremental Indexing

Only re-indexes new/modified files — no full rebuilds needed

Architecture

┌──────────────────────────────────────────────────────────────┐
│                    Unified Gateway (:8000)                    │
│              FastAPI + LangGraph Agent Pipeline               │
│           analyze → execute → aggregate → rank                │
├──────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌────────────────┐  │
│  │ Core MCP │ │Files MCP │ │ Browser  │ │   Code MCP     │  │
│  │          │ │          │ │   MCP    │ │                │  │
│  │• Notes   │ │• PDF     │ │• Chrome  │ │• Git commits   │  │
│  │• Search  │ │• DOCX    │ │• Edge    │ │• Repo stats    │  │
│  │• Events  │ │• Text    │ │• Firefox │ │• VSCode recent │  │
│  │• Save    │ │          │ │          │ │                │  │
│  └──────────┘ └──────────┘ └──────────┘ └────────────────┘  │
│                                                              │
│  ┌──────────────────┐ ┌──────────────────────────────────┐   │
│  │ Conversations MCP│ │ Knowledge Graph MCP              │   │
│  │                  │ │                                  │   │
│  │• Claude exports  │ │• Entity extraction (regex-based) │   │
│  │• ChatGPT exports │ │• Graph search (Neo4j)            │   │
│  │• Search & parse  │ │• Path finding                    │   │
│  └──────────────────┘ └──────────────────────────────────┘   │
│                                                              │
├──────────────────────────────────────────────────────────────┤
│ ChromaDB (vectors)  │ Neo4j (graph)  │ SQLite (events)      │
└──────────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

  • Python 3.11+

  • uv — Python package manager

  • Docker — for Neo4j (optional)

  • A supported browser (Chrome, Edge, or Firefox) — for browser history (optional)

1. Clone & Install

git clone https://github.com/Shaktisinhchavda/memory-mcp.git
cd memory-mcp
uv sync

2. Configure

cp .env.example .env
# Edit .env if needed — defaults work out of the box on any OS

3. Add Your Notes

Place markdown or text files in data/notes/:

echo "# My Project Ideas" > data/notes/ideas.md

4. Index & Build

# Index notes into ChromaDB (incremental — only new/modified files)
uv run python scripts/index_notes.py

# Full rebuild (if needed)
uv run python scripts/index_notes.py --force

# Start Neo4j (optional — for knowledge graph)
docker compose up -d

# Build knowledge graph from notes
uv run python scripts/build_graph.py

5. Run

Option A — Claude Desktop Integration (recommended)

Add to your Claude Desktop config:

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "memory-mcp": {
      "command": "uv",
      "args": ["--directory", "/path/to/memory-mcp", "run", "python", "core_mcp/server.py"]
    },
    "files-mcp": {
      "command": "uv",
      "args": ["--directory", "/path/to/memory-mcp", "run", "python", "connectors/files_mcp/server.py"]
    },
    "browser-mcp": {
      "command": "uv",
      "args": ["--directory", "/path/to/memory-mcp", "run", "python", "connectors/browser_mcp/server.py"]
    },
    "code-mcp": {
      "command": "uv",
      "args": ["--directory", "/path/to/memory-mcp", "run", "python", "connectors/code_mcp/server.py"]
    },
    "conversations-mcp": {
      "command": "uv",
      "args": ["--directory", "/path/to/memory-mcp", "run", "python", "connectors/conversations_mcp/server.py"]
    },
    "graph-mcp": {
      "command": "uv",
      "args": ["--directory", "/path/to/memory-mcp", "run", "python", "knowledge_graph/server.py"]
    }
  }
}

Replace /path/to/memory-mcp with the actual absolute path to this project.

Option B — Unified Gateway (HTTP API)

uv run uvicorn gateway.server:app --host 127.0.0.1 --port 8000

Open Swagger UI at http://localhost:8000/docs.

Tools Reference (25 tools)

Core MCP (6 tools)

Tool

Description

read_notes

List and read personal notes

semantic_search

Query knowledge base by meaning

get_recent_activity

Recent file change events

index_stats

Vector store diagnostics

save_note

Save new notes (memory write-back)

append_note

Append to existing notes

Files MCP (3 tools)

Tool

Description

read_local_file

Read PDF, DOCX, text files

list_local_files

List files in a directory

search_local_files

Search by filename pattern

Browser MCP (5 tools)

Tool

Description

recent_browsing_history

Recent history (Chrome/Edge/Firefox)

most_visited_sites

Top sites by visit count

search_browsing_history

Search by keyword

browsing_stats

Stats across all browsers

detected_browsers

List installed browsers

Code MCP (5 tools)

Tool

Description

recent_commits

Git commit history

commit_details

Single commit details

git_status

Modified/staged/untracked files

repo_statistics

Repo stats and contributors

vscode_recent

Recently opened in VSCode

Conversations MCP (3 tools)

Tool

Description

list_conversation_exports

List exported JSON files

read_conversations

Parse Claude/ChatGPT exports

search_in_conversations

Search by keyword

Note: Conversation support is export-based. Export your Claude or ChatGPT conversations as JSON and place them in data/conversations/. Live sync is not supported due to API limitations.

Calendar MCP (3 tools)

Tool

Description

upcoming_events

Future calendar events

todays_schedule

Today's events

search_events

Search events by keyword

Setup required: Create a Google Cloud project, enable the Calendar API, download credentials.json to config/, and run the server once to complete OAuth. See Google Calendar API Quickstart.

Knowledge Graph MCP (4 tools)

Tool

Description

graph_search

Find entity and its connections

find_connection

Shortest path between entities

graph_stats

Node/relationship counts

ingest_file

Extract entities into graph

Technical Details

Entity Extraction

The knowledge graph uses regex and pattern-based extraction (no LLM required):

  • People: Capitalized multi-word names (e.g., "John Smith")

  • Technologies: Matched against a curated keyword list (56 terms)

  • Topics: Extracted from markdown headers

  • Projects: Detected by naming patterns (e.g., "VizDataAI", "FastAPI")

  • Tasks: Parsed from - [ ] / - [x] markdown checkboxes

This is intentionally lightweight and local-only. For higher accuracy, swap in spaCy NER or an LLM-based extractor in knowledge_graph/extractor.py.

Incremental Indexing

Both index_notes.py and build_graph.py track file modification times in a manifest file. Only new or changed files are re-processed. Use --force for a full rebuild.

Browser Support

The browser connector auto-detects installed browsers:

Browser

Windows

macOS

Linux

Chrome

Yes

Yes

Yes

Edge

Yes

Yes

Yes

Firefox

Yes

Yes

Yes

Project Structure

memory-mcp/
├── core_mcp/                 # Phase 1 — Core MCP server
│   ├── server.py             # Main MCP server (stdio)
│   ├── tools/                # Notes + search + write-back tools
│   ├── vector_store/         # ChromaDB integration
│   └── event_logger/         # File watcher + SQLite
├── connectors/               # Phase 2 — Data connectors
│   ├── files_mcp/            # PDF, DOCX, text reader
│   ├── browser_mcp/          # Chrome, Edge, Firefox history
│   ├── calendar_mcp/         # Google Calendar (OAuth)
│   ├── code_mcp/             # Git + VSCode activity
│   └── conversations_mcp/    # Claude/ChatGPT exports
├── knowledge_graph/          # Phase 3 — Neo4j graph
│   ├── extractor.py          # Entity extraction (regex-based)
│   ├── graph_store.py        # Neo4j CRUD + search
│   └── server.py             # Graph MCP server
├── gateway/                  # Phase 4 — Unified gateway
│   ├── agent.py              # LangGraph orchestrator
│   ├── router.py             # Smart query router
│   ├── ranker.py             # Multi-signal ranking
│   ├── context.py            # PersonalContext model
│   └── server.py             # FastAPI gateway
├── scripts/                  # Utility scripts
│   ├── index_notes.py        # Build vector index (incremental)
│   ├── build_graph.py        # Build knowledge graph
│   └── start_watcher.py      # Start file watcher
├── data/                     # Your personal data (git-ignored)
│   ├── notes/                # Markdown notes
│   ├── files/                # Documents
│   └── conversations/        # Exported AI chats
├── docker-compose.yml        # Neo4j container
├── pyproject.toml            # Dependencies
└── .env.example              # Config template

Privacy

  • 100% local-first — all processing runs on your machine

  • No cloud APIs required — embeddings, search, and graph are local

  • Personal data never committed — data/ is git-ignored

  • Secrets excluded — .env, credentials, and tokens are git-ignored

  • Cross-platform — works on Windows, macOS, and Linux

Known Limitations

  • Conversations: Export-only (Claude/ChatGPT JSON). No live sync due to API restrictions.

  • Calendar: Requires a one-time Google Cloud setup for OAuth credentials.

  • Entity extraction: Uses structural heuristics (word count, length, non-name word filter) to reduce false positives. Still regex-based — for higher accuracy, swap in spaCy NER or an LLM-based extractor in knowledge_graph/extractor.py.

License

MIT

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license - permissive license
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maintenance

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