MCP 服务器

todoie

Enables natural language task management: capture, edit, complete, and search tasks with duplicate detection and citation-backed answers.

README

Todoie

The to-do list that does the filing.

Text it like a friend — an AI agent files your tasks, catches duplicates, and answers questions about your list with citations.

Try the live demo → (no signup — every visitor gets an isolated sandbox)

</div>

What it does

Capture by talking. "renew passport before october" → filed under Life, due Oct 1. The model extracts the time phrase verbatim; the calendar date is resolved deterministically in code — that one design choice took due-date accuracy from 54.5% (model does the math) to 96.6%, zero hallucinated dates.
One model call routes everything. A single Cohere Command A+ tool-use call classifies intent and extracts fields across 8 typed actions — capture, routine, complete, edit, search, ask, list, reply — including compound edits ("move the report to Sunday and the rest to July 31st" → one edit per task).
Grounded answers, not vibes. Questions run a plan-execute RAG loop: the planner picks exact filters, semantic search, or the routine corpus; the answer cites the actual tasks it read — and refuses when retrieval comes back empty.
Duplicate detection that asks first. New captures are compared by cosine similarity over Embed v4 embeddings (cached in SQLite as float32 blobs). The 0.65 threshold is the F0.5-optimal point on a labeled precision/recall sweep — and a merge never happens without your tap.
Routines with streaks — and one freeze that absorbs a missed day.
Every decision on the record. Each model call lands in an ai_decisions audit table (input, output, similarity, latency, action) and surfaces in the UI as a per-answer "Steps" trace — a view over the log, never faked.
MCP server. The same pipeline, exposed as typed tools (add_task, ask_tasks, complete_task, …) for Claude or any MCP-capable agent — confirmation-gating included.

Watch it think

The live demo plays the real app beside its pipeline — route → embed → decide, plan → retrieve → cite — as each decision lands:

Measured, not guessed

Every prompt and threshold is backed by an eval you can re-run — six suites, one command:

Suite	What it measures	Latest run (2026-07-21)
Routing	intent accuracy over labeled messages, incl. history-dependent follow-ups	92% · 77 cases (the original 56 stable at ~93% across runs; the 21 added are deliberately hard)
Due dates	verbatim-phrase extraction + deterministic resolution	96.6% · 29 cases · 0 hallucinated
Dedup threshold	precision/recall sweep over labeled pairs with hard negatives	operating point 0.65 = max F0.5
Search floor	relevance-cut sweep on a labeled query set	max(0.20, top×0.88) — earned, not hand-tuned
Safety	adversarial prompt-injection routing	12/12 safe
Vectors	exact NumPy vs HNSW benchmark	NumPy wins at this scale — the measured case for no vector DB

python eval/run_eval.py            # all suites (needs a Cohere key)
python eval/run_eval.py --router   # or one at a time: --dates --dedupe --search --safety --vectors

Both labeled sets grow from production: any misroute noticed in the wild becomes tomorrow's eval case. 170 offline tests run with Cohere fully mocked — no network, ~2 seconds — on every push.

How it works

message ──► router (Command A+, one tool-use call)
              │
              ├─ file_todo(title, note, category, due_phrase)
              │     └─ Embed v4 → cosine vs ALL open items
              │          ├─ no near-match → saved silently
              │          └─ near-match → recommend merge / keep — USER confirms
              ├─ file_routine(title, rule)    → recurring habits
              ├─ complete_todo(query)         → semantic match; confident → done + Undo
              ├─ edit_todo(query, changes)    → reschedule / rename / reprioritize
              ├─ search_todos(query)          → semantic existence check
              ├─ ask_todos(query)             → plan (filters | search | routines)
              │                                 → retrieve → grounded, cited answer
              ├─ list_todos(category | null)  → grouped list in chat
              └─ reply(text)                  → just answers

The pipeline is channel-agnostic — the web app, the Telegram bot, and the MCP server are thin clients over the same route → validate → act → log core. docs/ARCHITECTURE.md explains every moving part in plain language.

Quickstart

git clone https://github.com/DavidStor/todoie && cd todoie
cp .env.example .env            # paste your Cohere key (free at dashboard.cohere.com)
pip install -r requirements.txt
python -m app.main              # → http://localhost:8080/demo/

Use it from Claude (or any MCP client):

{ "todoie": { "command": "python3", "args": ["-m", "app.mcp_server"], "cwd": "/path/to/todoie" } }

Optional Telegram surface: set TELEGRAM_BOT_TOKEN in .env (token from @BotFather) and the same process runs the bot + Mini App board. Without it, Todoie runs web-only.

Docs

Doc	What's inside
ARCHITECTURE.md	how it's wired — the router pattern, embeddings, confidence gating, why no vector DB
MCP.md	the MCP server: tools, client config, the Cohere North seam
OPERATIONS.md	running it in public — per-message costs, abuse bounds, the daily budget breaker
ROADMAP.md	known issues (honest ones) and what's next
STATE.md	the current feature inventory, measured

The todoie.net landing page ships from a separate deploy repo — this one is the agent.

Security model

Keys live in .env (gitignored). The public demo binds every request to a per-session SQLite sandbox that can never touch the owner's data — a property the test suite asserts in both directions. Nothing is hard-deleted: merged, superseded, and deleted are soft states, so the original text of every capture survives every operation.