check_dependency_health

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

With no annotations provided, the description carries the burden of behavioral disclosure. It usefully adds the cost, the returned status values ('HEALTHY', 'DEGRADED', 'DOWN'), and failover routes, but it does not explain caching semantics (despite the force_fresh parameter), failure behavior, or side effects.

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?

The description is a single compact paragraph that front-loads cost, then states purpose, scope, and return values. Every clause adds useful information and there is no filler.

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?

The output schema covers return structure, but with no annotations and no parameter documentation, the description alone is not fully complete. It gives context for tool selection but leaves parameter usage and default/caching behavior unexplained.

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?

Schema description coverage is 0%, so the description must compensate for all three parameters. It mentions provider names in the prose but never explains how category, providers, or force_fresh should be populated, what values are valid, or how they interact.

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 names a specific action ('Check real-time health, latency, and operational status'), a clear resource ('upstream AI model providers... and Web3 RPCs'), and lists concrete target providers such as OpenAI, Anthropic, and Base. This makes it easy to distinguish from sibling tools, including probe_upstream_endpoint, because it clearly scopes the check to dependency health for AI and RPC services.

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 phrase 'before launching costly swarm workflows' clearly states when the tool should be used. However, it does not mention when not to use it or name alternatives such as probe_upstream_endpoint, so it stops short of explicit exclusion guidance.

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