context_list
Retrieve recent Claude Code contexts to review conversations, code snippets, suggestions, or errors across sessions.
Instructions
List recent Claude Code contexts
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results | |
| type | No | Filter by type (conversation, code, suggestion, error) |
Implementation Reference
- src/mcp_server/server.py:412-417 (handler)The handler logic for the 'context_list' tool within the call_tool method. It retrieves recent contexts using storage.list_contexts with optional limit and type filter, formats them, and returns as TextContent.
if name == "context_list": limit = arguments.get("limit", 20) type_filter = arguments.get("type") contexts = self.storage.list_contexts(type_filter=type_filter, limit=limit) result = self._format_contexts_response(contexts) return [TextContent(type="text", text=result)] - src/mcp_server/server.py:144-162 (schema)Input schema definition for the 'context_list' tool, including properties for limit and type filter.
Tool( name="context_list", description="List recent Claude Code contexts", inputSchema={ "type": "object", "properties": { "limit": { "type": "integer", "description": "Maximum number of results", "default": 20, }, "type": { "type": "string", "description": "Filter by type (conversation, code, suggestion, error)", "enum": ["conversation", "code", "suggestion", "error"], }, }, }, ), - src/mcp_server/server.py:104-382 (registration)The list_tools method registers the 'context_list' tool by including it in the returned list of Tool objects, which is hooked to the MCP server.
async def list_tools(self) -> list[Tool]: """List available tools.""" return [ # Context tools Tool( name="context_search", description="Search Claude Code contexts by query string or tags", inputSchema={ "type": "object", "properties": { "query": {"type": "string", "description": "Search query"}, "tags": { "type": "array", "items": {"type": "string"}, "description": "Filter by tags", }, "type": { "type": "string", "description": "Filter by type (conversation, code, suggestion, error)", "enum": ["conversation", "code", "suggestion", "error"], }, "limit": { "type": "integer", "description": "Maximum number of results", "default": 10, }, }, }, ), Tool( name="context_get", description="Get full details of a specific context by ID", inputSchema={ "type": "object", "properties": { "context_id": {"type": "string", "description": "Context ID"}, }, "required": ["context_id"], }, ), Tool( name="context_list", description="List recent Claude Code contexts", inputSchema={ "type": "object", "properties": { "limit": { "type": "integer", "description": "Maximum number of results", "default": 20, }, "type": { "type": "string", "description": "Filter by type (conversation, code, suggestion, error)", "enum": ["conversation", "code", "suggestion", "error"], }, }, }, ), Tool( name="context_delete", description="Delete a specific context by ID", inputSchema={ "type": "object", "properties": { "context_id": {"type": "string", "description": "Context ID to delete"}, }, "required": ["context_id"], }, ), Tool( name="context_save", description="Save a new context entry for the current project", inputSchema={ "type": "object", "properties": { "type": { "type": "string", "description": "Context type", "enum": ["conversation", "code", "suggestion", "error"], }, "title": {"type": "string", "description": "Context title"}, "content": {"type": "string", "description": "Context content"}, "tags": { "type": "array", "items": {"type": "string"}, "description": "Tags for categorization", }, "session_context_id": { "type": "string", "description": "Link to existing context ID", }, }, "required": ["type", "title", "content"], }, ), # Todo tools Tool( name="todo_search", description="Search todo snapshots by content or context description", inputSchema={ "type": "object", "properties": { "query": {"type": "string", "description": "Search query"}, "project_path": { "type": "string", "description": "Filter by project path", }, "limit": { "type": "integer", "description": "Maximum number of results", "default": 10, }, }, "required": ["query"], }, ), Tool( name="todo_get", description="Get full details of a specific todo snapshot by ID", inputSchema={ "type": "object", "properties": { "snapshot_id": {"type": "string", "description": "Todo snapshot ID"}, }, "required": ["snapshot_id"], }, ), Tool( name="todo_list", description="List recent todo snapshots", inputSchema={ "type": "object", "properties": { "limit": { "type": "integer", "description": "Maximum number of results", "default": 20, }, "project_path": { "type": "string", "description": "Filter by project path", }, }, }, ), Tool( name="todo_save", description="Save a new todo snapshot", inputSchema={ "type": "object", "properties": { "todos": { "type": "array", "items": { "type": "object", "properties": { "content": {"type": "string"}, "status": {"type": "string", "enum": ["pending", "in_progress", "completed"]}, "activeForm": {"type": "string"}, }, "required": ["content", "status", "activeForm"], }, "description": "List of todo items", }, "project_path": { "type": "string", "description": "Project path (defaults to current directory)", }, "context": { "type": "string", "description": "Description of what you're working on", }, "session_context_id": { "type": "string", "description": "Link to existing context ID", }, }, "required": ["todos"], }, ), Tool( name="todo_restore", description="Get todo snapshot for restoring (active snapshot or specific ID)", inputSchema={ "type": "object", "properties": { "snapshot_id": { "type": "string", "description": "Specific snapshot ID (optional, defaults to active snapshot)", }, "project_path": { "type": "string", "description": "Project path (used if snapshot_id not provided)", }, }, }, ), Tool( name="todo_delete", description="Delete a specific todo snapshot by ID", inputSchema={ "type": "object", "properties": { "snapshot_id": {"type": "string", "description": "Todo snapshot ID to delete"}, }, "required": ["snapshot_id"], }, ), # AI opinion tools Tool( name="ask_chatgpt", description="Ask ChatGPT a question about a context entry, or get a general second opinion", inputSchema={ "type": "object", "properties": { "context_id": {"type": "string", "description": "Context ID to ask about"}, "question": { "type": "string", "description": ( "Optional specific question to ask about the context. If not provided, gets a general second opinion." ), }, }, "required": ["context_id"], }, ), Tool( name="ask_claude", description="Ask Claude a question about a context entry, or get a general second opinion", inputSchema={ "type": "object", "properties": { "context_id": {"type": "string", "description": "Context ID to ask about"}, "question": { "type": "string", "description": ( "Optional specific question to ask about the context. If not provided, gets a general second opinion." ), }, }, "required": ["context_id"], }, ), Tool( name="ask_gemini", description="Ask Google Gemini a question about a context entry, or get a general second opinion", inputSchema={ "type": "object", "properties": { "context_id": {"type": "string", "description": "Context ID to ask about"}, "question": { "type": "string", "description": ( "Optional specific question to ask about the context. If not provided, gets a general second opinion." ), }, }, "required": ["context_id"], }, ), Tool( name="ask_deepseek", description="Ask DeepSeek a question about a context entry, or get a general second opinion", inputSchema={ "type": "object", "properties": { "context_id": {"type": "string", "description": "Context ID to ask about"}, "question": { "type": "string", "description": ( "Optional specific question to ask about the context. If not provided, gets a general second opinion." ), }, }, "required": ["context_id"], }, ), ] - src/mcp_server/server.py:611-625 (helper)Helper method used by the context_list handler to format the list of contexts into a readable string response.
def _format_contexts_response(self, contexts: list[ContextEntry]) -> str: """Format a list of contexts for response.""" if not contexts: return "No contexts found." lines = [f"Found {len(contexts)} contexts:\n"] for ctx in contexts: chatgpt_icon = "✓" if ctx.chatgpt_response else "○" claude_icon = "✓" if ctx.claude_response else "○" gemini_icon = "✓" if ctx.gemini_response else "○" deepseek_icon = "✓" if ctx.deepseek_response else "○" tags_str = f" [{', '.join(ctx.tags)}]" if ctx.tags else "" ai_icons = f"GPT:{chatgpt_icon} Claude:{claude_icon} Gemini:{gemini_icon} DeepSeek:{deepseek_icon}" lines.append(f"{ai_icons} [{ctx.type}] {ctx.title}{tags_str}\n ID: {ctx.id}\n Timestamp: {ctx.timestamp.isoformat()}\n") return "\n".join(lines)