celiums-memory

journal_arc

Build a coherent narrative across your recent journal entries using an LLM with anti-confabulation guardrails. Detects contradictions, outliers, and outputs a confidence score to reveal genuine thematic arcs.

Instructions

Build a coherent arc across YOUR recent entries using the configured LLM — with anti-confabulation guardrails. Output ALWAYS returns 4 keys: narrative, contradictions (entry pairs in tension), outliers (entries that don't fit), and confidence [0,1]. If outliers is empty you are probably confabulating coherence — the response is annotated with a WARNING. confidence < 0.7 is flagged as a "weak arc". Default window is the last month, max 50 entries. Excludes superseded entries.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "journal_arc"
  }
}

Input Schema

Name	Required	Description	Default
`window`	No	last_week \| last_month (default) \| all
`max_entries`	No	Default 50, max 200.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.2.5Apr 28, 2026

TDQS

A4.3/5.0

Behavior5/5

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

The description thoroughly discloses the tool's output format (narrative, contradictions, outliers, confidence), edge cases (empty outliers indicating confabulation, weak arc for confidence < 0.7), default window (last month), limit (50 entries), and exclusion of superseded entries. No annotations exist, so the description fully carries the behavioral burden.

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

Conciseness5/5

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

The description is a concise 5-sentence paragraph that front-loads the primary purpose and then efficiently covers output structure, warnings, defaults, and limits. Every sentence adds value without 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?

Given the tool has only 2 optional parameters and no output schema or annotations, the description provides comprehensive context: what the tool does, what it returns, when to suspect confabulation, and configuration defaults. This is sufficient for an AI to decide when to invoke the tool and interpret results.

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?

The input schema already provides descriptions for both parameters (window and max_entries), achieving 100% coverage. The description adds minor value by stating defaults ('default window is last month, max 50'), but does not significantly enhance understanding beyond the schema.

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 it builds a coherent arc from recent journal entries, with specific features like anti-confabulation guardrails and a defined output structure (4 keys). It distinguishes itself from sibling tools like journal_recall or journal_introspect by focusing on narrative construction rather than retrieval or self-reflection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for constructing a narrative arc from recent entries, but it does not explicitly state when to prefer this tool over siblings or when not to use it. The phrase 'using the configured LLM' hints at its unique capability, but no alternatives or exclusions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.