Closes the loop so דפנה's positions (written inline in the UI and
saved to analysis-and-research.md) automatically become binding
direction for the legal-writer agent — no manual copy-paste,
no bypass.
Backend:
- research_md.extract_chair_directions(path) returns a compact dict
with status (missing/empty/partial/complete), filled_count,
empty_count, and a reduced list of threshold_claims + issues each
with {id, number, title, direction}. Designed to be directly usable
as direction_doc by the writer.
- New MCP tool: drafting.get_chair_directions(case_number) wraps the
helper, resolves the case research file path via config.find_case_dir,
returns formatted JSON.
- Registered in server.py as mcp__legal-ai__get_chair_directions.
legal-writer agent update:
- Adds get_chair_directions to the tools list.
- New mandatory "שלב 1ב" before any block writing: call
get_chair_directions, branch on status.
- missing → halt, report "legal-analyst לא רץ עדיין"
- empty → halt, instruct Dafna to fill positions via the UI URL
- partial → halt unless user confirms; write only filled sections
- complete → proceed
- New "שלב 1ג" constructs an internal direction_doc from the
received chair rulings before writing block י.
- Block י section expanded with 5 binding rules:
1. Open each discussion with Dafna's ruling as the thesis
2. Frame the reasoning in her style (use get_style_guide phrases)
3. Match her tone (decisive vs nuanced)
4. Must NOT contradict her position — if she disagreed with your
own inclination, her position rules
5. Use legal_questions from the analysis file as the analytical
structure (principle question first, concrete application second)
- New bullet section for block יא: summarize each chair ruling
briefly, state final outcome, close with the signed date formula.
Verified all four status paths (missing/empty/partial/complete) via
local test. Now Dafna's workflow is fully end-to-end: she reads the
analyst report in the UI, fills "עמדת ועדת הערר" in each card, hits
blur to auto-save, then triggers legal-writer — which picks up her
positions as direction without any file shuffle.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New architecture: MCP provides context, Claude Code writes.
New functions:
- get_block_context(case_id, block_id) → returns full context package
(prompt, source docs, claims, direction, precedents, style guide)
WITHOUT calling Anthropic API
- save_block_content(case_id, block_id, content) → saves block to DB
New MCP tools: get_block_context, save_block_content
The old write_block (API-based) still works as fallback.
The new flow uses Claude Code's own model (Opus 4.6, 1M context)
which has no separate API billing.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add expected_outcome field to cases (rejection/partial/full/betterment_levy)
- New lessons.py module with golden ratios, templates, and drafting guidance per outcome type
- Style analyzer now uses Opus with full decision text (no truncation), with multi-pass fallback for large corpora
- Drafting tool provides outcome-specific templates, section guidance, and ratio comments
- Improved JSON extraction with bracket-matching fallback
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Ezer Mishpati - AI legal decision drafting system with:
- MCP server (FastMCP) with document processing pipeline
- Web upload interface (FastAPI) for file upload and classification
- pgvector-based semantic search
- Hebrew legal document chunking and embedding
