footprinter-cli 1.0.0rc1

Index your files, emails, browser history, and chats locally. Give AI agents controlled access via MCP.

Footprinter

Index your files, conversations, browser history, and more through connector plugins. Search across everything. Give AI assistants structured context via MCP.

Every conversation with an AI assistant starts from zero. It doesn't know what files you've been editing, what you were researching yesterday, or what conversations you had last week. You either paste context in manually or the AI operates blind.

Footprinter fixes that. It scans your work into a local SQLite database, links related items across sources, and exposes the result through MCP — so Claude Desktop, or any MCP-compatible assistant, can search your files, find related conversations, and understand the shape of your work. No copy-pasting. No cloud. Everything stays on your machine.

Install

pip install footprinter-cli

The base install includes the indexing pipeline, CLI, MCP server, HTTP API, and token encryption. Optional extras add more capabilities:

pip install footprinter-cli[full]       # All optional extras (semantic + parse)
pip install footprinter-cli[semantic]   # Semantic search (ChromaDB + ONNX embeddings)
pip install footprinter-cli[parse]      # PDF, Word, Excel, PowerPoint content extraction

Quick Start

fp setup     # Configure sources (interactive wizard)
fp ingest    # Index your files
fp status    # See what's indexed
fp search "meeting notes"   # Find things

macOS note: Browser history indexing requires Full Disk Access for your terminal app (System Settings > Privacy & Security > Full Disk Access).

Connect to Claude Desktop

Footprinter includes an MCP server that gives Claude Desktop (or any MCP client) structured access to your indexed data:

fp setup mcp    # Configure MCP for Claude Desktop

Once configured, Claude can search your files, browse projects, and find related conversations — through natural language.

What It Indexes

Source	What's captured
Local files	Path, type, size, timestamps, content hash
Browser history	Safari and Chrome — URLs, titles, visit times
Chat exports	Claude and ChatGPT conversation exports
Documents	PDF, Word, Excel, PowerPoint content (with [parse] extra)
Semantic embeddings	Conceptual similarity across all sources (with [semantic] extra)

Additional sources are available through connector plugins.

CLI Commands

All commands use the fp entry point.

Command	Purpose
fp setup	Configure sources and integrations
fp ingest	Run the indexing pipeline
fp status	System health and data counts
fp search	Search across all indexed sources
fp connect	Manage optional integrations
fp mcp	MCP server and access policies
fp api	Start the HTTP API server
fp view	Browse indexed data (files, folders, projects, clients, chats, emails, visits)
fp upsert	Create or update records and assign relationships
fp data	Export data, generate templates, or import metadata corrections
fp delete	Soft-delete a record
fp vectorize	Manage per-record vectorization control

Run fp <command> --help for full usage.

Connectors

Connector plugins add external data sources like email, cloud storage, and third-party services. They install alongside Footprinter and register automatically:

pip install footprinter-<name>

First-party and community connectors are in development — check the repository for updates.

Use fp connect list to see available connectors and their status.

Architecture

Single-process CLI with optional MCP server. SQLite database. No containers, no cloud, no accounts.

Sources are scanned into SQLite with bidirectional links connecting local files to remote backups via content hash matching. Embeddings are generated at ingest time for semantic search. The MCP server exposes indexed data with two-layer access control (visibility + permissions) — you decide what agents can see.

Optional Extras

Extra	What it adds
[semantic]	Semantic search via ChromaDB + ONNX embeddings
[parse]	PDF, Word, Excel, PowerPoint content extraction
[full]	All optional extras (semantic + parse)

Privacy note: The [semantic] extra installs ChromaDB, which bundles PostHog analytics. ChromaDB collects anonymous usage telemetry by default. Set ANONYMIZED_TELEMETRY=False in your environment to disable it. See ChromaDB telemetry docs for details.

Requirements

• Python 3.11+
• macOS or Linux
• Full Disk Access on macOS (for browser history)

Documentation

• Interfaces — CLI commands, MCP tools, Python API
• Data Model — database schema
• Pipeline — indexing stages and configuration
• Access Control — MCP security model

Contributing

Bug fixes, documentation, and tests welcome. For new features or architectural changes, open an issue first to discuss the approach.

Connector plugins use an internal API that isn't stable yet — we're not accepting connector contributions at this time.

Development setup

git clone https://github.com/swellcitygroup/footprinter.git
cd footprinter
python3 -m venv venv
./venv/bin/pip install -e ".[dev]"

Running tests

pytest tests/ -v --tb=short

Code style

• PEP 8
• Type hints on function signatures
• logging over print() in library code

Workflow

1. Fork the repository
2. Create a feature branch from main
3. Write tests (TDD preferred — tests before implementation)
4. Run the test suite
5. Submit a PR targeting main

Never commit API keys, tokens, or credentials. Report security vulnerabilities privately — see SECURITY.md.

Pull request expectations

• Tests must pass
• No breaking changes to existing CLI commands
• Fill out the PR template
• One logical change per PR

All PRs are reviewed by the maintainer. Expect reviews within one week. CI must pass before review begins.

No Contributor License Agreement required. By submitting a PR, you agree your contribution is licensed under the project's MIT License.

Community

• Code of Conduct
• Security Policy

License

MIT — see LICENSE.