Rename chat_completions to ask_question and revise input/output schema for clarity

Author: rodion-m

CLAUDE.md

@@ -48,7 +48,7 @@ This is a Model Context Protocol (MCP) server that provides AI clients with acce
 ### Core Components
 
 - **`codealive_mcp_server.py`**: Main server implementation using FastMCP framework
-- **Three main tools**: `chat_completions`, `search_code`, `get_data_sources`
+- **Three main tools**: `ask_question`, `search_code`, `get_data_sources`
 - **CodeAliveContext**: Manages HTTP client and API credentials
 - **Async lifespan management**: Handles client setup/teardown
 
@@ -63,7 +63,7 @@ This is a Model Context Protocol (MCP) server that provides AI clients with acce
 ### Data Flow
 
 1. AI client connects to MCP server via stdio/SSE transport
-2. Client calls tools (`get_data_sources` → `search_code` → `chat_completions`)
+2. Client calls tools (`get_data_sources` → `search_code` → `ask_question`)
 3. MCP server translates tool calls to CodeAlive API requests
 4. CodeAlive API returns semantic search results or chat completions
 5. Server formats and returns results to AI client

README.md

@@ -23,15 +23,15 @@ Once connected, you'll have access to these powerful tools:
 
 1. **`get_data_sources`** - List your indexed repositories and workspaces
 2. **`search_code`** - Semantic code search across your codebase  
-3. **`chat_completions`** - AI chat with full project context
+3. **`ask_question`** - AI chat with full project context
 
 ## 🎯 Usage Examples
 
 After setup, try these commands with your AI assistant:
 
 - *"Show me all available repositories"* → Uses `get_data_sources`
 - *"Find authentication code in the user service"* → Uses `search_code`
-- *"Explain how the payment flow works in this codebase"* → Uses `chat_completions`
+- *"Explain how the payment flow works in this codebase"* → Uses `ask_question`
 
 ## Table of Contents