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replica_restart

Determines if a specific Ray Serve replica can be restarted. If not, provides guidance to scale the deployment down/up or redeploy instead.

Instructions

[WRITE][risk=high] Restart one Serve replica — NOT available over Ray's REST API.

Ray Serve exposes no per-replica restart endpoint over REST; the controller respawns unhealthy replicas on its own. This tool refuses with a teaching error. To force a cycle, scale the deployment down then up, or redeploy. The dry_run preview reports the same unavailability rather than a false green.

Args: application: Serve application name. deployment: Deployment name. replica_id: Replica id (from replica_list). dry_run: If True, report availability without attempting a restart. target: Inference target name from config; omit for the default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNo
dry_runNo
deploymentYes
replica_idYes
applicationYes
Behavior5/5

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

With no annotations, the description fully discloses behavior: it refuses, never actually restarts, dry_run reports the same unavailability, and it avoids false positives. It also flags the risk level with '[WRITE][risk=high]' while explaining the action is not actually performed.

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 front-loaded with the key message ('NOT available'), followed by a concise explanation and alternatives. Every sentence adds value, and the parameter list is compact yet informative. No wasted words.

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?

For a tool that intentionally refuses all operations, the description is complete: it covers the purpose, the underlying technical limitation, the behavioral result (including dry_run), and all parameters. Despite lacking an output schema, the tool's behavior is fully specified.

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

Parameters5/5

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

Schema coverage is 0%, but the description compensates by explaining each parameter in context: replica_id comes from replica_list, dry_run reports availability without attempting, and target can be omitted for the default. This goes beyond the schema's bare property names.

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 uses a specific verb+resource pair ('Restart one Serve replica') and immediately clarifies that it is NOT available over REST, explicitly stating the tool refuses with a teaching error. This clearly distinguishes it from sibling tools like drain_replica and scale_replicas_up/down.

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

Usage Guidelines5/5

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

The description states that the tool is unavailable over REST, explains why (controller respawns replicas), and gives explicit alternatives: 'scale the deployment down then up, or redeploy.' This provides clear when-not-to-use guidance and alternative tools.

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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