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

No annotations exist, so the description carries the full burden and meets it: it discloses that the tool takes no arguments, can be large, may return an {error} when no graph has been built, omits pages the caller cannot open, and costs no AI tokens. Size and failure-mode disclosure are exactly what an agent needs for a potentially heavy call.

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?

Front-loaded with the purpose, then scope, then alternatives, then return shape and cost. Every clause earns its place — the sibling pointers and the token-cost note each change agent behavior.

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?

Though an output schema exists (so return values need not be spelled out), the description still summarizes the shape and documents the empty-graph error case and the exclusion of unopenable pages. Combined with the no-args and cost notes, an agent has everything needed to call it correctly.

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 schema has zero parameters and 100% coverage, so the baseline is 4. The description reinforces this by stating it 'takes no arguments', which prevents an agent from hunting for a filter parameter.

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?

States a specific verb and resource: 'Return the workspace's knowledge graph: topics, links and clusters.' It further differentiates from siblings by naming get_page for page text and impact for code dependencies, so an agent can route correctly without opening any schema.

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?

Explicit when-to-use ('to draw or reason about the overall shape of the brain — which topics are hubs, how areas connect') and explicit when-not ('not to read content'), with the two alternative tools named for each excluded case. Nothing is left to inference.

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