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checkpoint_session

Create a recovery snapshot of your work state to save progress and enable a fresh AI to continue from a clear goal, current state, and next action.

Instructions

Create a recovery snapshot of the current work state. Call after meaningful milestones, before ending work, or before switching to another major task. Include enough state (goal, current_state, next_action) that a fresh AI could continue from this point without needing the full conversation history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNoWhat we're trying to accomplish
session_idNoOptional: specific session ID
next_actionNoRecommended next action
current_stateNoWhere we are now
workspace_pathNoWorkspace path to resolve session from

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.2.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden. It explains what a snapshot is and what content it should include, but doesn't disclose whether the snapshot persists, whether it creates a new checkpoint or overwrites a previous one, or whether it requires an active session. Adequate but not rich.

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?

Three sentences with no filler. The main action is front-loaded, followed by precise usage timing, then a concrete instruction on what content to include. Every sentence earns its place.

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 persistence-oriented tool with no output schema, the description explains the intended content and the use cases. It doesn't describe return values or side effects, but the agent has enough to invoke it correctly and populate the parameters meaningfully.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by identifying the critical fields (goal, current_state, next_action) and explaining their collective purpose: enabling a fresh AI to continue without full history. This goes beyond the schema's individual field 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 states a specific verb ('Create a recovery snapshot') and a clear resource ('current work state'). This clearly differentiates it from session lifecycle siblings like start_session or end_session, which manage the session itself rather than preserving a resume point.

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?

It gives explicit timing guidance: 'after meaningful milestones, before ending work, or before switching to another major task.' It doesn't explicitly name alternatives or state when not to use it, but the provided context is strong enough for an agent to decide correctly.

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