recall_memory_tool
Retrieve relevant information from project memory using keywords, questions, or statements to support decision-making and maintain context.
Instructions
从项目记忆中回忆相关信息
Args:
interest: 想要回忆的任何东西，可以是一句陈述、一个问题甚至是关键词Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| interest | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/memory_mcp/server.py:22-29 (handler)The primary handler function for the 'recall_memory_tool' MCP tool. It is registered via @mcp.tool() decorator and delegates the core logic to recall_memory.
@mcp.tool() async def recall_memory_tool(interest: str) -> str: """从项目记忆中回忆相关信息 Args: interest: 想要回忆的任何东西，可以是一句陈述、一个问题甚至是关键词 """ return await recall_memory(interest, _registry) - src/memory_mcp/tools/recall.py:10-59 (helper)Helper function implementing the recall logic using a small LLM agent with ListMemoriesTool, ReadMemoryTool, and a submit tool for generating the final report.
async def recall_memory(interest: str, registry: MemoryRegistry) -> str: """使用 small_agent 实现的回忆流程""" list_tool = ListMemoriesTool(registry) read_tool = ReadMemoryTool(registry) final_tools: list[ToolUnionParam] = [ { "name": "submit", "description": "提交回忆报告", "input_schema": { "type": "object", "properties": { "report": { "type": "string", "description": "综合回忆报告（Markdown 格式）", } }, "required": ["report"], }, } ] initial_prompt = f"""尝试从记忆库回忆与这个有关的信息：{interest} 请按以下指导处理： 1. 使用 list_memories 工具搜索相关记忆 2. 使用 read_memory 工具读取相关记忆 3. 基于读取的内容，调用 submit 提交综合报告 提示： - list_memories 返回的是按匹配度排序的结果 - read_memory 需要提供每个记忆唯一的关键词组 - 报告除了直接回应用户的兴趣点，还应该包含补充详细的背景信息 - 如果没有相关内容，请报告'没有相关记忆'""" result = await small_agent( initial_prompt=initial_prompt, tools=[list_tool, read_tool], final=final_tools, maxIter=10, ) if result is None: return "查询超时，未能生成报告" tool_name, tool_input = result if tool_name == "submit": return tool_input.get("report", "") return "未知错误"