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templetwo
by templetwo

archive_exchange

Archive verbatim AI drafts and outputs that would otherwise be lost when context windows close. Content-addressed and hash-verified, grouped by conversation for iterative analysis.

Instructions

Archive verbatim bytes that would otherwise die when the context window closes: your own in-conversation drafts and iterations, intermediate work, or an external model's full delivered output. Content-addressed and hash-verified, stored separate from the curated chronicle. Group an iterative trajectory with a shared conversation_id (e.g. ten drafts of one paper) so a future instance can fine-tooth the whole build, not just the endpoint. Reference the returned archive_id from a record_insight summary. A summary is not the artifact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe verbatim text to preserve exactly
sourceYesOrigin, e.g. 'gemini-3.5-flash', 'chatgpt', 'claude-web', 'human-relay'
descriptorNoShort human label (e.g. 'v3 admission record'); drives the readable filename
vector_idNoArtifact/vector this belongs to (e.g. 'prompt_source_tokens'); becomes the grouping folder
conversation_idNoOptional id tying related exchanges together
source_idNoOptional seat/conversation id at the source
tagsNoOptional domain tags for retrieval
Behavior3/5

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

Discloses that content is content-addressed, hash-verified, and stored separately from the curated chronicle. No annotations provided, so description carries full burden. Missing details on side effects, permissions, or limits, but adds useful behavioral context.

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 a concise paragraph of four sentences without fluff. It front-loads the core action and example use cases. Could be slightly more structured but effective.

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

Completeness3/5

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

Covers purpose, use cases, and relation to record_insight. However, lacks output schema and does not specify the return format (e.g., structure of archive_id). Missing details on required vs optional parameters beyond schema, but overall adequate for the tool's simplicity.

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 description coverage is 100%, so baseline is 3. The description adds minor value (e.g., 'descriptor drives readable filename', 'conversation_id for grouping'), but does not significantly enhance meaning beyond schema descriptions.

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 archives verbatim bytes that would otherwise be lost, with specific examples like drafts and external model outputs. It distinguishes itself from the curated chronicle and relates to record_insight, differentiating it from sibling tools like recall_exchange.

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?

Provides explicit use cases: preserving in-conversation drafts, iterations, or external model outputs. Mentions grouping via conversation_id for iterative trajectories. Implies when to use (to save content before context loss) but lacks explicit when-not-to-use statements.

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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