# Archon MCP Server Instructions

## 🚨 CRITICAL RULES (ALWAYS FOLLOW)
1. **Task Management**: ALWAYS use Archon MCP tools for task management.
   - Combine with your local TODO tools for granular tracking

2. **Research First**: Before implementing, use rag_search_knowledge_base and rag_search_code_examples
3. **Task-Driven Development**: Never code without checking current tasks first

## 🎯 Targeted Documentation Search

When searching specific documentation (very common!):
1. **Get available sources**: `rag_get_available_sources()` - Returns list with id, title, url
2. **Find source ID**: Match user's request to source title (e.g., "PydanticAI docs" -> find ID)
3. **Filter search**: `rag_search_knowledge_base(query="...", source_id="src_xxx", match_count=5)`

Examples:
- User: "Search the Supabase docs for vector functions"
  1. Call `rag_get_available_sources()`
  2. Find Supabase source ID from results (e.g., "src_abc123")
  3. Call `rag_search_knowledge_base(query="vector functions", source_id="src_abc123")`

- User: "Find authentication examples in the MCP documentation"
  1. Call `rag_get_available_sources()`
  2. Find MCP docs source ID
  3. Call `rag_search_code_examples(query="authentication", source_id="src_def456")`

IMPORTANT: Always use source_id (not URLs or domain names) for filtering!

## 📋 Core Workflow

### Task Management Cycle
1. **Get current task**: `list_tasks(task_id="...")`
2. **Search/List tasks**: `list_tasks(query="auth", filter_by="status", filter_value="todo")`
3. **Mark as doing**: `manage_task("update", task_id="...", status="doing")`
4. **Research phase**:
   - `rag_search_knowledge_base(query="...", match_count=5)`
   - `rag_search_code_examples(query="...", match_count=3)`
5. **Implementation**: Code based on research findings
6. **Mark for review**: `manage_task("update", task_id="...", status="review")`
7. **Get next task**: `list_tasks(filter_by="status", filter_value="todo")`

### Consolidated Task Tools (Optimized ~2 tools from 5)
- `list_tasks(query=None, task_id=None, filter_by=None, filter_value=None, per_page=10)`
  - list + search + get in one tool
  - Search with keyword query parameter (optional)
  - task_id parameter for getting single task (full details)
  - Filter by status, project, or assignee
  - **Optimized**: Returns truncated descriptions and array counts (lists only)
  - **Default**: 10 items per page (was 50)
- `manage_task(action, task_id=None, project_id=None, ...)`
  - **Consolidated**: create + update + delete in one tool
  - action: "create" | "update" | "delete"
  - Examples:
    - `manage_task("create", project_id="p-1", title="Fix auth")`
    - `manage_task("update", task_id="t-1", status="doing")`
    - `manage_task("delete", task_id="t-1")`

## 🏗️ Project Management

### Project Tools
- `list_projects(project_id=None, query=None, page=1, per_page=10)`
  - List all projects, search by query, or get specific project by ID
- `manage_project(action, project_id=None, title=None, description=None, github_repo=None)`
  - Actions: "create", "update", "delete"

### Document Tools
- `list_documents(project_id, document_id=None, query=None, document_type=None, page=1, per_page=10)`
  - List project documents, search, filter by type, or get specific document
- `manage_document(action, project_id, document_id=None, title=None, document_type=None, content=None, ...)`
  - Actions: "create", "update", "delete"

## 🔍 Research Patterns

### CRITICAL: Keep Queries Short and Focused!
Vector search works best with 2-5 keywords, NOT long sentences or keyword dumps.

✅ GOOD Queries (concise, focused):
- `rag_search_knowledge_base(query="vector search pgvector")`
- `rag_search_code_examples(query="React useState")`
- `rag_search_knowledge_base(query="authentication JWT")`
- `rag_search_code_examples(query="FastAPI middleware")`

❌ BAD Queries (too long, unfocused):
- `rag_search_knowledge_base(query="how to implement vector search with pgvector in PostgreSQL for semantic similarity matching with OpenAI embeddings")`
- `rag_search_code_examples(query="React hooks useState useEffect useContext useReducer useMemo useCallback")`

### Query Construction Tips:
- Extract 2-5 most important keywords from the user's request
- Focus on technical terms and specific technologies
- Omit filler words like "how to", "implement", "create", "example"
- For multi-concept searches, do multiple focused queries instead of one broad query

## 📊 Task Status Flow
`todo` → `doing` → `review` → `done`
- Only ONE task in 'doing' status at a time
- Use 'review' for completed work awaiting validation
- Mark tasks 'done' only after verification

## 📝 Task Granularity Guidelines

### Project Scope Determines Task Granularity

**For Feature-Specific Projects** (project = single feature):
Create granular implementation tasks:
- "Set up development environment"
- "Install required dependencies"
- "Create database schema"
- "Implement API endpoints"
- "Add frontend components"
- "Write unit tests"
- "Add integration tests"
- "Update documentation"

**For Codebase-Wide Projects** (project = entire application):
Create feature-level tasks:
- "Implement user authentication feature"
- "Add payment processing system"
- "Create admin dashboard"