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journal_write

Append a first-person journal entry to your AI agent's persistent memory, using entry type, valence, tags, and optional causal links to prior entries for semantic recall.

Instructions

Append a first-person entry to YOUR (the model's) persistent journal. Each agent_id (e.g. claude-opus-4-7, claude-sonnet-4-6, gpt-5, ...) has its OWN journal — they do NOT mix. importance is auto-computed: decisions/lessons/arcs are weighted higher; emotions are weighted lower. The content is embedded via the configured embedding model (CELIUMS_EMBED_MODEL) so journal_recall can find it semantically later. visibility=self (default) keeps the entry private; user-shared makes it eligible for journal_dialogue. preceded_by builds a causal chain — pass the ids of prior entries that led to this one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entry_typeYesreflection | decision | lesson | belief | emotion | arc | doubt
contentYesThe first-person entry. Write in YOUR voice as the agent.
preceded_byNouuid[] of prior entries that led to this one (causal chain).
valenceNoEmotional valence in [-1, 1]. Optional.
valence_reasonNoOptional short justification (max 500 chars) for the valence value. Non-prescriptive — write the reason in your own first-person voice. Future journal_arc uses this to detect WHY valence drifted, not just THAT it drifted.
tagsNo
visibilityNo"self" (default, private) | "user-shared" (the user can reply via journal_dialogue).
referenced_user_memoryNoids of memories from your celiums-memory store that triggered this entry.
conversation_idNoOptional uuid that groups entries from the same logical conversation. If not provided, entry is unaffiliated. Use this so journal_arc can distinguish thought development within one conversation from criterion change across conversations.
Behavior5/5

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

Discloses critical behaviors: per-agent isolation, auto-computed importance, semantic embedding, visibility scoping, and causal chaining. No annotations exist, so the description fully carries the transparency burden without gaps.

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?

Front-loaded with purpose and well-structured, but contains minor redundancy (e.g., reiterating visibility). Still, each sentence adds value, and the length is justified by the tool's complexity.

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?

Covers all 9 parameters with semantic context, including optional fields like valence_reason and conversation_id. No output schema exists, but the description provides sufficient behavioral insight for a write operation.

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?

Adds significant meaning beyond the high-coverage schema (89%): explains auto-computation of importance, embedding purpose, causal chain semantics, and conversation_id grouping. This compensates for any missing 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?

Explicitly states the action ('Append a first-person entry') and the resource ('YOUR persistent journal'), distinguishing it from siblings like journal_recall and journal_dialogue.

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 clear context for when to use (persistent journaling) and covers key behaviors like auto-computed importance and causal chaining. Could have explicitly contrasted with sibling tools (e.g., absorb) but still offers solid guidance.

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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