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memos_search

Retrieve memories from a personal knowledge base by searching with natural language queries. Results can be filtered to human notes only.

Instructions

Search MemOS for memories matching a query.

Returns the top matches as a markdown list with score, source, category, project, and content excerpt. Searches the same scope that Paperclip agents read from, so results include both human notes and any agent KB entries (completed issues, executive briefs, indexed documents).

Args: query: Natural language query, e.g. "rename routine convention". top_k: Maximum number of results to return (default 5, max 25). only_human: If True, filter results to entries with [source: human]. Use this when you specifically want notes Seth wrote, not agent runs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
only_humanNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It mentions the scope (same as Paperclip agents) and that results are a markdown list, but does not explicitly state read-only behavior or any side effects.

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 well-structured with a clear first sentence, a paragraph explaining return format and scope, and an argument list. Every part adds value without redundancy.

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 there is an output schema, the description appropriately does not detail return values. It covers parameter semantics and scope well, but lacks mention of idempotency or auth requirements, though these are less critical for a search tool.

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

Parameters5/5

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

With 0% schema coverage, the description adds detailed meaning for all three parameters: 'query' (natural language), 'top_k' (default 5, max 25), and 'only_human' (filter to human notes), significantly enhancing understanding.

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 tool searches memories by query, and distinguishes it from siblings 'memos_recent' and 'memos_remember' by specifying it returns top matches.

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?

The description implies usage context (search vs. recent vs. remember) but does not explicitly state when to use alternatives or when not to use this tool.

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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