backend_status

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

The read-only, idempotent, non-destructive annotations already cover safety. The description adds valuable behavioral context by stating that the tool explains why an engine cannot run and distinguishes 'model declined' from missing configuration, which is beyond what the annotations convey.

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?

Two sentences, no filler. The first sentence states the tool's purpose, and the second provides the critical usage trigger and interpretation guidance. Every word earns its place.

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?

For a zero-parameter status tool with an output schema, the description is complete: it names the resource, the failure scenarios it disambiguates, and when to invoke it. Nothing needed for correct invocation is 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, so the schema fully covers the input surface. The baseline of 4 applies; no additional parameter explanation is necessary or possible.

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 states exactly what the tool reports: which decision engines can run and why not. It distinguishes this status/diagnostic tool from the decision-execution siblings by framing it as the diagnostic step before decision tools are used.

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?

It explicitly tells the agent when to call this tool: 'Call this first when a decision tool fails.' It also explains how the result guides the next action by separating two distinct failure causes, which is directly actionable.

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