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delete_episode

Delete a specific episode and all its Hebbian associations, retaining only a metadata tombstone for audit. Use for accidentally recorded PII or fundamentally wrong recordings.

Instructions

Delete a single episode by ID. Use this for content that should not exist: accidentally recorded PII, sensitive data, or fundamentally wrong recordings. Do NOT use for factual corrections — record a new episode with the correction instead and let compression resolve it. Deletion cascades: all Hebbian associations involving the episode are removed, and the deletion is logged in the audit trail. By default, a tombstone is preserved as an existence proof for audit integrity: episode ID, original timestamp, episode type, and SHA-256 content hash are retained — the original text is fully erased. Under GDPR framing, the retained fields are pseudonymized metadata, not content; disable tombstones at Store construction (keep_tombstones=False) if even this metadata must not survive. The hash chain remains verifiable either way. This action is irreversible.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
episode_idYesThe 8-character hex ID of the episode to delete. Use recall or prepare_wrap to find episode IDs.
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses cascading deletion of Hebbian associations, audit trail logging, tombstone preservation with specific retained fields (episode ID, timestamp, type, hash), irreversibility, and even GDPR framing and configuration option. This is comprehensive behavioral disclosure.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and is concise given the complexity. Each sentence adds necessary context (cascading, tombstone, audit). However, it is somewhat lengthy for a simple delete operation, but the added detail is justified.

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 the complexity of the deletion operation (cascading effects, tombstone policy, audit trail), the description is highly complete. It explains consequences and configuration options. No output schema exists, but the description adequately covers the side-effect behavior.

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 100% with a single parameter 'episode_id' already described as '8-character hex ID' with guidance to use recall or prepare_wrap. The description adds minimal value beyond the schema for parameter semantics, merely reiterating 'by ID'.

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 specific verb 'delete' and resource 'episode by ID'. It distinguishes from siblings by explicitly noting when to use this tool (for content that should not exist like PII) and when not to (for factual corrections, suggesting alternative 'record').

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?

Explicitly provides when to use (accidentally recorded PII, sensitive data, fundamentally wrong recordings) and when not to use (factual corrections), and names the alternative tool 'record'.

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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