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forget_subject

Erase all memories about a subject, scrub its references from survivors, and return a deletion receipt. Use dry-run first to preview inherited erasures and resolve ambiguous subjects.

Instructions

Right-to-erasure by SUBJECT (GDPR Art.17 / DSR): delete every memory about subject AND scrub its id from survivors' links/supersession pointers, so it can't resurface via recall or consolidation. basis records the legal/operational reason. Returns a receipt (forgotten count, ids, scrubbed_links) you can keep as evidence.

RUN IT WITH dry_run=True FIRST. This cascades through inherited lineage, so it commonly erases more than the records that name the subject: the preview returns {would_erase, direct, inherited, sample, also_carrying} and changes nothing. inherited is the count you cannot predict, and also_carrying names the OTHER subjects whose data goes down with this request — one erasure is quietly several more often than not.

If the call raises AmbiguousSubject, the subject you passed canonicalizes to the same key as a DIFFERENT source in the store (e.g. two people under one host: crm.example.com/alice and crm.example.com/bob), so erasing would delete a third party's records. Read the message, confirm which subject is meant, and then choose: exact=True erases only the records whose RAW source string is this subject (plus their lineage) and LEAVES the colliding subject alone — prefer it, it completes the DSAR without touching anyone else. allow_ambiguous=True erases every colliding subject together, so pass it only if you really mean that. This surface used to offer allow_ambiguous alone and this text named it as THE answer, which pointed the caller at the over-deleting half of the choice; measured, that erased a third party's record where exact=True kept it. Collisions are not rare: canonicalisation is host/collection level, so 'employee/1001' and 'employee/1002' share a canonical form.

authorized_by (the authorising principal's public key) and authorization (their signature over erasure_challenge(subject, request_id)) are recorded in the tombstone's auth field — the Art.30 record of WHO authorised the deletion. Neither was on this surface, so every MCP erasure was unattributed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
basisNo
exactNo
dry_runNo
subjectYes
request_idNo
authorizationNo
authorized_byNo
allow_ambiguousNo
Install Server

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description fully carries the behavioral burden and does so thoroughly. It discloses cascading deletion through inherited lineage, unpredictable inherited counts, third-party data being erased, collision behavior, dry-run semantics, and attribution requirements. It even admits past design flaws where the tool over-deleted, which is exceptional transparency.

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 long but well-structured: main behavior, dry-run safety, ambiguity handling, and authorization are laid out in logical progression. Every sentence adds safety-critical value, though some historical context about the old allow_ambiguous surface could be trimmed without losing core guidance.

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?

For a complex, high-stakes tool with 8 parameters, no annotations, and no output schema, this description is remarkably complete. It covers the return receipt shape, dry-run preview fields, error behavior, collision handling, auth recording, and the irreversible/cascading nature of the operation. An agent has enough information to invoke this tool correctly and avoid data-loss incidents.

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 must compensate, and it does. It explains subject, basis, dry_run, exact, allow_ambiguous, authorized_by, authorization, and request_id in meaningful operational terms, including how they interact during ambiguous collisions and how they are recorded in the tombstone.

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 names a specific verb ('delete every memory') and resource ('about subject') and clearly distinguishes this tool from the broader 'forget' and 'forget_pii' siblings by emphasizing subject-scoped erasure with lineage scrubbing. It also states the GDPR context and what the tool returns, leaving no ambiguity about its function.

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 gives strong operational guidance: run with dry_run=True first, prefer exact=True in ambiguous cases, and use allow_ambiguous=True only when intended. It clearly explains when to choose different flag settings, though it does not explicitly compare against sibling tools like forget_pii or forget, so it stops short of full alternative routing.

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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