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save_continuity

Validate and save compressed continuity markdown. Checks section structure, citation authenticity, and detects gaming, then records associations between episodes.

Instructions

Validate and save the compressed continuity file. Call this after compressing your episodes using the instructions from prepare_wrap. The text must contain exactly 4 sections: ## State, ## Patterns, ## Decisions, ## Context. The server validates structure, checks graduation citations against real episodes (cited IDs must exist), checks explanation overlap (evidence must reference actual episode content), detects citation gaming (suspicious reuse of single episodes), and may demote ungrounded graduations. Also records Hebbian associations between co-cited episodes (episodes cited together on the same pattern line form strong links; episodes cited in the same wrap form weaker links) and decays unreinforced associations. Returns validation results, association metrics, and section sizes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe full continuity markdown to save. Must contain all 4 required sections.
affective_stateNoOptional: your functional state during this compression. Reflect on how you felt while consolidating — engaged, curious, uncertain, frustrated, calm, etc. This creates persistent emotional associations between co-cited episodes and modulates link strength (high engagement = stronger associations). Provide tag (free text label) and intensity (0.0-1.0).
wrap_tokenNoOptional: the 32-char hex session-handshake token from the prepare_wrap response (the 'Wrap token: <hex>' line at the end of the text). Pass it back here to verify you are saving the wrap you prepared — a mismatch (stale or wrong token) raises an error instead of silently committing against the wrong wrap. The frozen-snapshot filter automatically applies whenever prepare_wrap established a snapshot; this token argument is the optional explicit verification layer for integration environments that can round-trip the value.
allow_shrinkNoOptional (default false): override the catastrophic-shrink gate. By default a wrap that collapses a protected memory layer — the timeless felt section, the graduating identity section, or the whole continuity — is refused as a likely recency-trap / stateless-reset failure. Set true ONLY for a deliberate diet / migration recompression that intentionally shrinks memory.
Behavior5/5

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

No annotations provided, so description carries full burden. It comprehensively details validation rules, citation checks, gaming detection, Hebbian association recording, shrink gate behavior, and token verification. Leaves no behavioral ambiguity.

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?

Rather long but well-structured and front-loaded with core purpose. Every sentence conveys important information. Could be slightly tighter, but no 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?

Despite complexity (4 params, nested object, no output schema), the description covers expected return values ('validation results, association metrics, and section sizes'), all parameter behaviors, and usage context. No gaps.

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

Parameters4/5

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

Schema coverage is 100% with detailed descriptions. The description adds meaningful context beyond schema: e.g., effect of affective_state on association strength, wrap_token mismatch error, allow_shrink gate conditions. Adds value to already good parameter docs.

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?

Clearly states the verb (validate and save), resource (compressed continuity file), and context (after compressing episodes using prepare_wrap). Distinguishes itself from sibling tools like prepare_wrap, delete_episode, recall, etc.

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?

Explicitly specifies when to use: after compressing episodes from prepare_wrap. Details required input structure and validation steps. Lacks explicit when-not-to-use or alternatives, but context is sufficiently clear.

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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