Save the current state of the learned model to configured persistence immediately. Use this when you need to preserve training progress or synchronize stored data.
Write an immediate snapshot of the learned model to configured persistence.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| saved | Yes | ||
| state_path | Yes | ||
| cluster_count | Yes | ||
| total_messages | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, destructive, and idempotent hints, so the bar is lower. The description adds meaningful context by specifying that the operation writes an 'immediate snapshot' and where it goes ('configured persistence'), clarifying synchronous behavior and the persistence target beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, efficient sentence that front-loads the action ('Write') and avoids any filler. Every word contributes to identifying what the tool does and where the output goes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, an output schema, and annotations covering idempotency and destructiveness, the description provides enough context for an agent to correctly invoke save_state. No critical usage information appears missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema coverage is 100%, so there is no parameter documentation burden. The description adds semantic clarity about what is being saved, which is sufficient for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Write') and identifies the exact resource ('snapshot of the learned model') and destination ('configured persistence'). This clearly distinguishes save_state from the logging and stats sibling tools, which concern training/match logs rather than model state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used when an immediate, explicit save of the learned model is needed, and no sibling tool competes for this role. However, it does not state when to use it versus alternatives or mention any prerequisites, so usage guidance remains implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.