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

Adds real context beyond the readOnlyHint/destructiveHint=false annotations: that validation is free and no credits are debited, and that the result is a list of issues classified by rule and severity. It does not explain whether validation is enforced before a run or how severity should be acted on, keeping it below 5.

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 two lead sentences are tight and front-load the purpose, usage condition, and cost. The trailing example is long and partially redundant with the schema's $defs, which dilutes an otherwise efficient description.

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?

With an output schema present, the description needn't detail return values, and annotations cover the safety profile; the description fills in cost and trigger conditions well. Minor gap: the meaning and handling of 'severity' levels is left 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 100%, so the schema already documents both parameters; the description goes further with a concrete worked example of a valid graph shape. It offers no guidance on the optional api_key beyond what the schema states.

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?

Uses a specific verb ('checks a graph') with an enumerated set of what it looks for (cycles, dangling edges, unknown distributions, missing refs) and a stated output shape (issues by rule and severity). It is clearly distinguishable from run_simulation, which actually executes the graph.

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?

Explicitly states when to use it: 'when you built or edited a graph by hand and want to catch mistakes before spending a run on it,' which implicitly routes the agent away from run_simulation. No explicit when-not or mention of the estimate/run_simulation alternatives by name, so it falls short of a 5.

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