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amber_search_deleted_memories

Search through deleted memories to find information the user has previously removed. Filter by age or topic and restore relevant results.

Instructions

Search within the trash for soft-deleted memories. Useful when the user asks about something they've since deleted. Results are ordered by relevance (higher score = better match; scores are relative within a single query). Optional max_age_days restricts to recently deleted items. Optional topics filters by topic (semantic matching). Content is truncated to 1000 chars — use amber_get_memory for full content. Use amber_restore_memory to bring a result back. Rate-limited (search bucket: 5000 capacity, refills ~1 per 17 seconds).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language question or description of what to find. Use full sentences, not keywords.
n_resultsNoMaximum results (default 10, max 100).
metadata_filterNoOptional metadata filter.
max_age_daysNoOnly consider memories deleted within the last N days.
topicsNoOptional topic filter. Pass category names like 'food', 'work', 'beliefs'. Matched semantically.
Behavior5/5

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

With no annotations, the description fully discloses behavior: searches trash, relevance ordering, score meaning, optional filters, content truncation to 1000 chars, and rate limits (5000 cap, ~1 per 17s refill). No contradictions.

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 concise and well-structured: purpose, usage context, result ordering, optional parameters, truncation note, related tools, rate limits. Every sentence adds value without redundancy.

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

Completeness5/5

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

Given no output schema, the description explains return ordering and score meaning. Covers all critical aspects (5 params, 1 required, nested objects) thoroughly. No gaps.

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?

Input schema has 100% coverage, so baseline 3. Description adds minor nuance: query should be full sentences, default n_results=10, topics use semantic matching. Not significantly beyond schema.

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 within trash for soft-deleted memories, and specifies when to use it (user asks about deleted items). It distinguishes from siblings like amber_search_memories and amber_list_deleted_memories.

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

Usage Guidelines4/5

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

The description provides explicit context on when to use (user asks about deleted items), result ordering, and optional filters. It references sibling tools for related actions (get full content, restore). Could be improved by explicitly stating not to use for non-deleted memories.

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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