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save_cursor_memory

Saves summarized conversations and technical information to memory files with structured markdown formatting for organized retrieval and reference in Cursor IDE.

Instructions

Save conversation summary or important information to Cursor memory files. IMPORTANT: The model should first summarize the conversation or information into a well-formatted document before calling this tool. The content should be properly structured with markdown formatting for better readability.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoCategory for organization: 'conversation' (对话记录), 'documentation' (技术文档), 'code-patterns' (代码模式), 'project-notes' (项目笔记)conversation
contentYesWell-formatted and summarized content in markdown format. Should include: main points, key insights, code examples (if any), decisions made, and actionable items. The model should process and structure the information before saving.
tagsNoRelevant tags for better searchability (e.g., ['react', 'hooks'], ['api', 'design'], ['meeting', 'decisions'])
titleYesClear, descriptive title for the memory entry (e.g., 'React Hooks 学习总结', 'API 设计讨论摘要')
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that this is a write operation ('Save'), implies persistence to files, and specifies formatting requirements (markdown, structured content). However, it lacks details on permissions, error handling, or file storage behavior, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by critical usage instructions. Both sentences earn their place by providing essential guidance without redundancy, making it efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description adequately covers the tool's purpose and usage. It addresses key aspects like content formatting and pre-call summarization. However, it could improve by mentioning potential side effects or response expectations, slightly reducing completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, only reinforcing that content should be 'well-formatted and summarized' in markdown, which aligns with schema details. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Save') and resource ('conversation summary or important information to Cursor memory files'), distinguishing it from siblings like 'fetch_link_documentation' (retrieving) and 'get_cursor_memories' (reading). It specifies the type of content being saved, making the purpose explicit and distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage instructions: 'The model should first summarize the conversation or information into a well-formatted document before calling this tool.' It also implies when to use it (for saving summarized content) versus alternatives like 'get_cursor_memories' for retrieval, offering clear guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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