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Edlineas

AIVectorMemory

by Edlineas

recall

Retrieve relevant memories using semantic vector search, finding related information even when terms differ. Filter results by scope, tags, or source for precision.

Instructions

语义搜索回忆记忆。通过向量相似度匹配,即使用词不同也能找到相关记忆。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo按标签过滤。query+tags 时默认 OR 匹配(任一标签命中即可),仅 tags 时默认 AND 匹配(精确分类浏览)
tierNo只搜索指定层级的记忆
briefNo精简模式:true 时只返回 content 和 tags,省略 id/session_id/created_at 等元数据,适合启动加载场景节省上下文
queryNo搜索内容(语义搜索,可选)
scopeNoall
top_kNo返回结果数量
sourceNo按来源过滤:manual=项目知识, experience=归档经验。不传则不过滤
tags_modeNo标签匹配模式:any=任一匹配,all=全部匹配。默认智能选择(query+tags→any,仅tags→all)
expand_relationsNo沿 related 关系扩展 1 跳查找相关记忆
exclude_supersededNo排除已被替代的记忆(已弃用,被替代的记忆现在通过 importance 降权自然排序,不再硬过滤)
Behavior3/5

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

The description discloses the key behavior of vector similarity matching, but with no annotations provided, it omits important traits like nondestructive nature, rate limits, or output details. It partially compensates by mentioning the core algorithm.

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 extremely concise with two sentences, front-loading the core purpose. Every word is necessary and adds value, leaving no redundancy.

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

Completeness3/5

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

With 10 parameters and no output schema, the description does not explain return format or provide usage context for advanced options like 'expand_relations' or 'exclude_superseded'. It is adequate for basic understanding but incomplete for full tool utilization.

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 coverage is 90%, so the schema already documents most parameters. The description adds no additional parameter meaning beyond the schema's descriptions, which are detailed. The brief description does not repeat parameter info but also does not enhance it.

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

Purpose4/5

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

The description clearly states the tool performs semantic search on memories using vector similarity, making its purpose specific and distinguishable from storage or retrieval tools like 'remember' or 'readme'. However, it could be more explicit about the resource being searched.

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

Usage Guidelines3/5

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

No explicit guidance on when to use this tool versus alternatives like 'graph' or 'remember'. The semantic search capability implies use for fuzzy matching, but the description lacks contrast with siblings or conditions for non-use.

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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