TOON V5 — CAOS Cognitive Agent Operating System. 18 engines, 14 agents, real Hermes/DeepSeek execution, SQLite FTS5 memory, council deliberation, discipline gate, 5 MCP graph tools.
ToonGine's memory tools have reasonable naming (verb_noun pattern) and descriptions (100-200 chars), but critical gaps undermine production readiness. Input schemas are visible and typed, but lack enums for constrained fields (memory_type, min_severity). Descriptions omit WHEN to use each tool and prerequisites. No output schema documentation. Error handling is absent, no guidance on what to do if a memory lookup fails or returns empty. The memory_store_decision tool is the only write operation but lacks confirmation/dry-run pattern. Tools are well-composed (single responsibility) but lack pagination despite claiming to search 'months ago' of data. No security model documented (who can access whose memories?). Baseline: 549 production tools average 18-char names (these are 15-20 chars, good) and 194-char descriptions (these are 120-180 chars, acceptable but sparse). 100% of A+ tools document return types; this server documents none.
Recall past conversations. 'What did we talk about last session?'
Recall all decisions made. 'What did we decide about the database?'
Find past mistakes before starting a task. Prevents repeating errors from months ago.
Recall all memories from a specific time period. 'What happened in March?'
Search ALL agent memories including from months ago. Use when you need to remember something from the past.
Get memory statistics — how much does this agent/store remember?
Store an important decision for institutional memory. Other agents can recall this later.
No output schema documentation. LLMs cannot plan downstream calls or extract required fields. All 7 tools return dicts but structure is undocumented.
Constrained parameters lack enums. 'memory_type' accepts free-form strings (mistake, decision, episodic, etc.), should be enum. 'min_severity' accepts 1-5 but no validation. LLMs will hallucinate invalid values.
No error handling or recovery guidance. If memory_search returns 0 results, what should the LLM do? If memory_store_decision fails, is it retryable? No error classification.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 54 | 2026-07-28+ | v2 |
memory_store_decision (write operation) lacks confirmation/dry-run pattern. Agents can irreversibly store decisions without preview. No idempotency guarantee documented.
No pagination support despite tools claiming to search 'months ago' of data. memory_search has 'limit' param but no offset/cursor or total_count in response. Large result sets will blow context windows.