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forget

Delete stored memories matching a query key. Irreversibly removes incorrect, sensitive, or outdated information from long-term memory.

Instructions

Delete stored memories that match a query key.

IMPORTANT: Deletion operates on short, canonical keys. The LLM MUST issue forget calls using the same concise, embedding-optimized, space-separated key style used to create memories (otherwise relevant memories may not be found). Prefer 1–5 words separated by spaces when requesting deletions.

This tool SHOULD be called by the LLM when the user explicitly requests that certain stored information be forgotten or removed (for example: "forget that I live in Paris") or when the assistant decides a memory must be purged because it is incorrect or sensitive.

Parameters:

  • key: concise, canonical, space-separated query text used to find candidate memories to delete.

  • top_k: number of nearest matches to consider for deletion.

Behavior:

  • Deletion is irreversible; the LLM should confirm with the user when intent is ambiguous before invoking this tool.

  • The tool returns deleted_ids for the memories that were removed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYes
top_kNo
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses irreversibility, the need to confirm ambiguous intent, return format (deleted_ids), and the critical key-style constraint. This goes well beyond basic expectations.

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 clear sections and front-loaded purpose. Each sentence serves a purpose, and the IMPORTANT note is strategically placed. No unnecessary filler.

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?

Despite no annotations or output schema, the description covers purpose, usage, parameters, behavior, and return value. It fully addresses the tool's complexity and compensates for missing structured information.

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?

Schema description coverage is 0%, so the description fully compensates by explaining both parameters: 'key' as concise canonical query text and 'top_k' as number of nearest matches. This adds meaningful context beyond the raw 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 deletes stored memories matching a query key, using a specific verb and resource. It distinguishes itself from sibling tools (store, recall) as the deletion counterpart.

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 scenarios for when to use the tool (user requests forgetting, assistant purging incorrect/sensitive data) with concrete examples. It does not explicitly mention alternatives like 'recall' for retrieval, but the context is clear enough.

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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