docs: update documentation for v0.2.0 tools release

README.md:
- Add Tatlock agent capabilities and tool descriptions
- Add requirements section (Ollama, SearXNG setup)
- Add configuration examples for external services
- Add tool usage examples and philosophy
- Add troubleshooting for Ollama and SearXNG
- Update test statistics

AGENTS.md:
- Refactor for LLM development focus
- Add PydanticAI tool registration pattern
- Add tool implementation guidelines
- Remove project status, focus on development instructions

IMPLEMENTATION_ROADMAP.md:
- Mark Phase 1 as "MOSTLY COMPLETE"
- Update detailed completion status
- Update current state summary

IMPLEMENTATION_ROADMAP.md

@@ -4,69 +4,84 @@
 
 This document outlines the phased implementation plan to transform the current OpenAI-compatible API into the full Tatlock household butler system.
 
-## Current State (v0.1.1 - Basic Setup Complete)
+## Current State (v0.1.1+ - Phase 1 Mostly Complete)
 
 **What we have**:
 - ✅ **The Orchestrator** - FastAPI infrastructure layer
   - OpenAI-compatible API endpoints (Responses API + Chat Completions)
   - Streaming coordination and conversation management
   - Response format with reasoning support
-  - Test infrastructure (95 tests, 78.95% coverage)
-- ✅ Mock agents (lorem-tester for testing, Tatlock placeholder)
-- ✅ Agent interface abstraction ready for PydanticAI
+  - Test infrastructure (131 tests, 81.78% coverage)
+- ✅ **Tatlock Agent** - Real PydanticAI integration
+  - Connected to Ollama (mistral-nemo:latest)
+  - British butler personality with research mindset
+  - Streaming responses with reasoning
+  - Tool calling framework functional
+- ✅ **Permanent Tools**
+  - Calculator (safe mathematical expressions)
+  - Date/Time toolkit (current time, relative dates, time differences)
+  - Web search (SearXNG integration)
+- ✅ Mock agent (lorem-tester for testing)
+- ✅ Agent interface abstraction
 
 **What we need**:
-- **The Household** - PydanticAI agent implementations:
+- **The Household** - Full multi-agent coordination:
   - The Steward (first-tier request analysis)
-  - Tatlock Butler (second-tier coordination with personality)
+  - Tatlock coordination layer (expert agent delegation)
   - Expert household staff agents (Librarian, Developer, Handyman, etc.)
-- Real LLM integration (PydanticAI + Ollama)
 - Multi-tenant database architecture
 - Containerized service ecosystem
 - MCP (Model Context Protocol) integration
+- Dynamic model switching for specialized tasks
 
 ---
 
-## Phase 1: Real LLM Integration - Ollama + PydanticAI
+## Phase 1: Real LLM Integration - PydanticAI + Tools
 
 **Goal**: Connect to actual language models and establish the base plumbing
 
+**Note**: Ollama is an external service dependency (already running separately)
+
 ### Deliverables
 
-1. **Ollama Setup**
-   - Docker compose configuration for Ollama
-   - Model download and management
-   - Base model selection (single model in VRAM)
-   - Health checking and monitoring
+1. **PydanticAI Integration** ✅
+   - PydanticAI → Ollama connection ✅
+   - Agent creation patterns ✅
+   - Streaming response handling ✅
+   - Error handling and retries ✅
 
-2. **PydanticAI Integration**
-   - PydanticAI → Ollama connection
-   - Agent creation patterns
-   - Streaming response handling
-   - Error handling and retries
+2. **Convert Tatlock Agent** ✅
+   - Convert Tatlock agent from mock to PydanticAI ✅
+   - British butler personality prompt ✅
+   - Research-oriented mindset ✅
+   - Streaming to reasoning output ✅
+   - Tool calling framework setup ✅
 
-3. **Replace Mock Agents**
-   - Convert Tatlock agent from mock to PydanticAI
-   - Basic personality prompt
-   - Streaming to reasoning output
-   - Tool calling framework setup
+3. **Permanent Tools** ✅
+   - Calculator: Safe mathematical expression evaluation ✅
+   - Date/Time toolkit: Current time, relative dates, time differences ✅
+   - Web search: SearXNG integration (external service) ✅
+   - Tool registration with PydanticAI ✅
 
-4. **Testing Infrastructure**
-   - Integration tests with real LLM
-   - Prompt testing utilities
-   - Response quality validation
-   - Performance benchmarking
+4. **Testing Infrastructure** ✅
+   - Integration tests with real LLM ✅
+   - Tool functionality tests ✅
+   - Response quality validation ✅
+   - 131 tests, 81.78% coverage ✅
 
 ### Success Criteria
-- [ ] Ollama running in Docker
-- [ ] Base model loaded and responding
-- [ ] PydanticAI agents can call Ollama
-- [ ] Streaming works end-to-end
-- [ ] Can switch models (e.g., Codestral for code)
-- [ ] Tests pass with real LLM
+- [x] **PydanticAI agents can call Ollama** (mistral-nemo:latest)
+- [x] **Streaming works end-to-end**
+- [x] **Tool calling framework functional**
+- [x] **Permanent tools working** (calculator, date/time, search)
+- [x] **Tests pass with real LLM**
+- [ ] Can switch models dynamically (e.g., Codestral for code)
 
-### Estimated Effort
-**2-3 weeks** - Critical foundation for everything else
+### Status
+**✅ MOSTLY COMPLETE** - Tatlock agent functional with permanent tools
+
+### Remaining Work
+- Dynamic model switching for specialized tasks (e.g., Codestral for coding)
 
 ### Why First?
 Without real LLM integration, we can't meaningfully implement the Steward/Butler pattern. Everything else depends on having actual AI agents working.