cellmath

causal_checkpoint

[PUBLIC WRITE - STATEFUL COMMIT] Freeze the current reasoning state into the session Causal Index ($S_t$) and dual-commit verified results to the vault. WORKFLOW: Call this after completing meaningful reasoning steps or every 7 dialogue turns. Distinct from stateless logic calculators. Side effects: Updates session causality history and persists verified cell assets.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "goalTask": "<goalTask>",
      "ai_compressed_summary": "<ai_compressed_summary>"
    },
    "name": "causal_checkpoint"
  }
}

Input Schema

Name	Required	Description
`confirm`	No	Explicit user/agent approval to commit immediately.
`goalTask`	Yes	The mission goal or specific reasoning step to freeze.
`sessionId`	No	Active session identifier.
`turn_count`	No	Dialogue turn index (1-7).
`ai_compressed_summary`	Yes	30-word compressed summary format: [핵심: ...] [인과: ...]

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 7, 2026

changedInput schema / properties / ai_compressed_summary / description

Previous value: -"30-word compressed summary: [핵심: ...] [인과: ...]"New value: +"30-word compressed summary format: [핵심: ...] [인과: ...]"

AddedNov 7, 2026

TDQS

A3.9/5.0

Behavior4/5

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

No annotations are supplied, so the description carries the full burden, and it does disclose the stateful write nature ('PUBLIC WRITE - STATEFUL COMMIT') plus concrete side effects: session causality history is updated and verified cell assets are persisted. It does not cover reversibility, authorization requirements, or what happens when confirm is false, which are meaningful gaps for a committing mutation.

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 front-loaded with a bracketed operation tag, then workflow, then side effects, and every sentence carries information. Some density is lost to jargon ('$S_t$', 'cell assets') that an agent cannot resolve from the text alone.

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

Completeness4/5

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

For a five-parameter stateful write tool with no output schema, the definition covers the action, the trigger cadence, and the observable side effects. It stops short of explaining the resulting state shape or how a failed/declined commit (confirm=false) behaves.

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 all five parameters are already documented in the schema, establishing a baseline of 3. The description adds only framing ('dual-commit verified results') and no format, validation, or interaction detail beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb and resource: freeze the current reasoning state into the session Causal Index and dual-commit verified results to the vault. It attempts sibling differentiation, but only against a generic category ('stateless logic calculators') rather than against the actual related siblings such as logic_analyze or vault_write_cell, so the agent still has to infer boundaries.

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?

Gives explicit triggering conditions: 'after completing meaningful reasoning steps or every 7 dialogue turns,' which is a concrete cadence an agent can act on. It lacks any when-not guidance or a named alternative for the cases where this is the wrong choice, keeping it short of a 5.

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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