Fundus

An evidence and slide archive for research work, with an MCP server for AI assistants.

Fundus keeps what research produces so that it is not repeated in every project: figures and time series, forecasts, studies, quotes, practice examples and qualitative facts – each with its source – plus the slides and charts built from them, with versions, decks and a verification stage. AI coding assistants (Claude Code, Codex, Gemini CLI, Cursor …) reach it through an MCP server: they look in Fundus first, reuse what is there with its source, and write new, sourced findings back. A browser portal shows the archive to people.

Everything runs locally on your machine: Python and SQLite are enough. Two optional services can be connected – nobody has to:

• Your own language model (Ollama, LM Studio, vLLM, a llama.cpp server or any OpenAI-compatible endpoint) adds meaning-based, cross-lingual search, descriptions of datasets and photos, aspect groups in the slide search and free-text questions about your datasets. You choose the models; fundus llm check tests the connection.

• Gemini on Vertex AI (Google Cloud, paid by use) reviews slides as an independent AI reviewer – numbers against sources, logic, language. fundus vertex check tests the setup.

The installing assistant asks about both at installation and at every update (AGENTS.md, section 6).

Status: Fundus was built and is used daily on Linux (Ubuntu). macOS and Windows are untested so far. The installation guide covers them step by step, but expect rough edges – issues and pull requests are welcome.

Features

• Evidence archive – observations (numbers with unit, period, geography), time series that grow edition by edition, forecasts that are later held against reality, studies, quotes, practices and facts; every item points to a document with URL, publisher and date. Public and internal visibility per item.

• Search – full-text search out of the box; with a language model also semantic and hybrid search across languages (a German question finds English evidence).

• MCP server – about forty fundus_* tools over streamable HTTP: search, overview by industry/topic, series, add/update, forecasts, slides, feedback, datasets, photos.

• Slide archive – slides and charts (HTML + PNG) with versions, slide sets, deck orders, copies, archive locks, trash, favourites and the sources and data points each slide uses.

• Verification – a feedback inbox per slide, review stages from "unchecked" to "confirmed", optional review by people (Fundus Review web app) and by an AI reviewer.

• Datasets – CSV (also .csv.gz), Excel, JSON and Parquet tables stored with Parquet copies, profiled with DuckDB, described and searchable, queried read-only with SQL; a catalog with coverage and archive views, usage log, lifecycle (active, superseded, withdrawn), reference datasets that stay in place, fixity checks and citations.

• Research registry – hypotheses with a frozen pre-registration (timestamp and hash; changes only as recorded deviations), results with every verdict, interpretations, and exploratory findings with their evidence.

• Media archive – photos and videos (HEIC, RAW, video via ffmpeg) with rights per item; optional local description with a vision model.

• Slide packages and Fundus Cloud – send slide sets to other Fundus installations as a package file or end-to-end encrypted through the optional Fundus Cloud; received sets are reviewed before they enter the archive.

• Data hygiene – duplicates, spelling variants, contradictions and a unit standard, with undo.

• Portal – a browser UI for slides, sets, decks, reviews, datasets, photos and statistics.

• Command line – fundus … for everything the tools do, for scripts and timers.

No screenshots here on purpose: a screenshot of a real archive would show its contents.

Related MCP server: NotebookLM REST API + MCP server

Quick start

git clone https://github.com/falkue/fundus-app.git fundus
cd fundus
python3 -m venv .venv
.venv/bin/pip install -e ".[render]"
.venv/bin/python -m playwright install chromium-headless-shell
.venv/bin/fundus stats                 # creates an empty archive and prints counts
.venv/bin/fundus serve                 # MCP server + portal on http://127.0.0.1:8010

Then open http://127.0.0.1:8010/portal/ and register the MCP server in your assistant, e.g. Claude Code:

claude mcp add --transport http fundus http://127.0.0.1:8010/mcp

On Windows use .venv\Scripts\ instead of .venv/bin/. The full, verified procedure for Linux, macOS and Windows – including autostart, local models and troubleshooting – is in AGENTS.md. The easiest way to install is to ask your AI coding assistant: "Install Fundus by following AGENTS.md in this repository."

Documentation

Your data stays yours

The repository contains code only – no slides, datasets, photos or evidence. Your archive lives in the data directory (data/ in the clone by default, or $FUNDUS_DB), which is ignored by git. Never commit or upload it.

Licence

See LICENSE.