The last open item from the plan. Missions can now draft invoices from
chat context, mark customer payments, and read status for autonomous
follow-up cadences.
Tool catalog (packages/shared-ai/src/tools/schemas.ts)
- create_invoice (propose) — clientName + lines[] + currency + due
- mark_invoice_paid (propose) — by id, optional back-dated paidAt
- list_invoices (auto) — with status + limit filter
- get_invoice_stats (auto) — open/overdue/YTD per currency
Had to widen the tool-parameter type vocabulary so create_invoice can
declare lines as a typed array. Touched three places:
- ToolSchema-side: the catalog's `type` string is already free-form so
'array' / 'object' just pass through
- ModuleTool-side (apps/mana/apps/web/src/lib/data/tools/types.ts): added
'array' | 'object' to the union so TS doesn't narrow the executor's
param signatures
- function-schema translator (packages/shared-ai): mapParamType +
JsonSchemaProperty both gained the two new types; the catalog-typo
guard test now uses 'fruit' as its sentinel (array no longer unknown)
Executor (apps/mana/apps/web/src/lib/modules/invoices/tools.ts)
- coerceLines accepts either a real array or a JSON-stringified array
(planners vary), skips malformed entries, converts major→minor units
- create_invoice pulls the generated number back from Dexie so the
success message shows "Entwurf 2026-0042 …" — the user recognises it
- mark_invoice_paid normalises YYYY-MM-DD → ISO so the store's timestamp
invariant (ISO throughout) stays intact
- list_invoices derives overdue on read (consistent with useAllInvoices),
returns major-unit amounts so the LLM reasons in user-facing numbers
- get_invoice_stats returns counts + open/overdue/YTD per currency
Registration: invoicesTools added to tools/init.ts. mana-ai drift guard
is happy (41/41 green); webapp + shared-ai type-check 0 errors; full
invoice test suite 59/59 green.
Closes: docs/plans/invoices-module.md §M8. All plan milestones now DONE.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Single bridge between the AI_TOOL_CATALOG shape and the wire format every
provider (Gemini, OpenAI-compat, Ollama ≥ 0.3) speaks for native tool
calling. Keeps the catalog as the source of truth — the runner never
reads catalog entries directly; it asks this converter for function-spec
shapes to hand the LLM.
- No _rationale or wrapper-tool injection: the runner doesn't need it
and the added schema noise would hurt planner quality.
- Throws on unknown parameter types so catalog typos (e.g. "array"
instead of "string") fail loudly instead of coercing silently.
- Preserves enum constraints; drops the enum key entirely when absent
so Gemini doesn't reject empty-enum function-declarations.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Completes the Quiz CRUD surface for the AI agent. Five new tools:
- update_quiz (propose) — rename/archive/pin + description/category
- update_quiz_question (propose) — text, type+options, explanation;
rejects a type swap without a matching optionsJson
- delete_quiz_question (propose) — symmetric to add_quiz_question
- get_quiz_questions (auto) — lets the planner see existing questions
before appending more (avoids duplicates)
- get_quiz_stats (auto) — attemptCount / avgScore / bestScore /
lastAttemptAt; enables adaptive missions like "analyze my weak spots
and generate harder questions"
delete_quiz deliberately left out — too destructive to leave in the
AI's hands when the user can delete manually in two clicks.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Quiz is now an AI-accessible module. The agent can mint empty quizzes
and append questions across all four types (single / multi / truefalse
/ text) via a single add_quiz_question tool whose optionsJson payload
shape is documented in the catalog description. list_quizzes (auto)
returns decrypted metadata so the planner can reference existing
quizzes when extending them. Enables missions like "baue ein Quiz aus
meinen Notizen zu Thema X" — planner reads via list_notes, proposes
create_quiz, then N × add_quiz_question.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Add discover_events (auto) and suggest_event (propose) to shared-ai
tool catalog. discover_events reads the discovery feed, suggest_event
creates a proposal to save a discovered event to the user's calendar.
- Add Event-Scout agent template with daily "Events der Woche" mission.
Policy: discover_events=auto, suggest_event=propose, all else denied.
- Add frontend tool implementations in events/tools.ts — discover_events
calls the feed API, suggest_event delegates to discoveryStore.saveEvent.
- Add feedback.ts — computes implicit user profile from save/dismiss
history (category affinity + source quality as 0–2x weight multipliers).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New module providing weather data for the DACH region via three sources:
- Open-Meteo (DWD ICON-D2 model) for current conditions and 7-day forecast
- DWD warnings endpoint for severe weather alerts
- Rainbow.ai / Open-Meteo fallback for minute-level rain nowcast
Includes API proxy with in-memory caching, Svelte 5 UI with location
picker, hourly/daily forecast, alert cards, and precipitation bar chart.
Two AI tools (get_weather, get_rain_forecast) enable the companion to
answer weather questions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add a guardrail system that runs alongside the Mission Runner pipeline
to catch obvious issues before they waste tokens or corrupt data.
Architecture (packages/shared-ai/src/guardrails/):
- types.ts: Guardrail, GuardrailResult, 4 phase interfaces
- builtin.ts: 4 built-in guardrails (always active):
- input-size-limit: blocks >100K chars of resolved input
- plan-step-limit: blocks plans with >25 steps (runaway planner)
- duplicate-destructive-tool: warns if undo_drink called 2x
- empty-required-params: blocks create_task without title
- runner.ts: runPrePlanGuardrails/runPostPlanGuardrails/runPreExecuteGuardrails
Wired into runner.ts at 3 checkpoints:
- Before deps.plan() — pre-plan check
- After plan received — post-plan check
- Before each stage() call — pre-execute check
Guardrails are synchronous, never hit the network, and produce
clear error messages when they block.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Introduce AI_TOOL_CATALOG in @mana/shared-ai as the single source of truth
for all 29 tool schemas (17 propose + 12 auto). Both the webapp policy and
the server-side mana-ai planner now derive their tool lists from the catalog
instead of maintaining independent hardcoded copies.
- New: packages/shared-ai/src/tools/schemas.ts — catalog with ToolSchema type
- Rewrite: proposable-tools.ts — derived from catalog instead of hardcoded array
- Rewrite: services/mana-ai/src/planner/tools.ts — 277→30 lines (imports from catalog)
- Simplify: webapp policy.ts — derives AUTO/PROPOSE from catalog defaultPolicy
Adding a new tool now requires 2 files instead of 3-5:
1. Add schema to AI_TOOL_CATALOG (shared-ai)
2. Add execute function in the module's tools.ts (webapp)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
