feat: enrich tool instructions, system prompts, and error hints ...

1. models.py — enriched Field descriptions with:
   - ✓/✗ query examples in descriptions
   - ILIKE semantics for city/district/metro
   - currency+price coupling warnings
   - rooms_count: 0=studio note
   - pagination strategy hints
   - Removed min_similarity from SearchSimilarInput (now hardcoded 0.7 in tools.py)

2. tools.py — added intelligent response layers:
   - _build_fallback_hint() — returns strategy tips on 0 results
     (switch tool, drop filters, simplify query, check language)
   - _build_invalid_query_hint() — detects stop-words & short queries,
     returns formatting tips with good/bad examples
   - describe_schema() enriched with:
     - usage_guidelines (when vector vs FTS)
     - query_examples (good/bad for both search types)
     - filter_combinations (recommended / avoid)
     - pagination_strategy

3. main.py — enriched tool docstrings with:
   - "When to use" vs other tools
   - Filter best practices
   - Fallback behavior description
   - Added @mcp.prompt() 'search_guide' — full agent guide
   - Added module docstring with server purpose, data scope, language

4. test_models.py — updated for removed min_similarity,
   added tests for description hints presence

10 tests pass, ruff clean.

Co-Authored-By: Claude <noreply@anthropic.com>

docs(README): clarify startup, config, and DB requirements ...

- Add step-by-step local and Docker launch instructions
- Expand Configuration section with required vs optional vars
- Add new 'Требования к базе данных' section:
  - Required PostgreSQL extensions (vector, pg_trgm)
  - Full property_listings column reference table
  - SQL for creating HNSW and GIN indexes
  - SQL for generating search_vector as stored tsvector
  - SQL hints for populating embedding column

Co-Authored-By: Claude <noreply@anthropic.com>

docs: comprehensive README + API reference + architecture guide ...

- Rewrite README.md with full feature list, data model, deployment,
  security, troubleshooting
- Add docs/API.md with JSON schemas, request/response examples,
  and 3 real-world usage scenarios
- Add docs/ARCHITECTURE.md with component diagram, data flows,
  DB index rationale, defense-in-depth security, scaling notes

Co-Authored-By: Claude <noreply@anthropic.com>

feat: implement MCP server for real estate database search ...

Add streamable-http MCP server with 4 tools:
- search_similar_listings: vector search via pgvector+HNSW (cosine)
- search_by_metadata: full-text search via FTS (ukrainian config)+GIN
- get_listing_by_id: fetch single listing by ID
- describe_schema: static schema documentation for AI agent

Features:
- asyncpg read-only pool with default_transaction_read_only
- parameterized queries + query safety check (multi-statement block)
- Pydantic models for input validation and JSON serialization
- Ollama nomic-embed-text client with dimension validation
- Error handling: Ollama/DB/validation exceptions wrapped in JSON
- Docker & docker-compose support
- ruff clean + 8 pytest tests passing

Co-Authored-By: Claude <noreply@anthropic.com>

Initial commit