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

The annotations already provide readOnlyHint=true and destructiveHint=false, and the description adds substantial behavioral context: it scans all knowledge graphs, bundles external signals from Google Trends/Wikipedia/Amazon/earnings, and aggregates work that normally requires dozens of separate searches. It doesn't mention latency or cost implications, but for a read-only aggregation tool this is a strong addition beyond the annotations.

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 well structured: it opens with the core action, follows with a compact list of return components, then provides usage examples and the value proposition. Slightly long, but each sentence earns its place and the most important scoping information ('ALL knowledge graphs') is front-loaded.

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?

Given the significant complexity of this tool—no output schema, five parameters, cross-graph and external signal aggregation—the description covers the return shape well. It lists nine output categories and a clear use case. A more detailed description could include how 'bundled supplemental signals' are delivered or whether results are paginated, but the description is sufficient for an agent to understand and invoke the tool 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?

Schema description coverage is 100%, so the schema already documents all 5 parameters. The description only adds brand-name example queries like 'Nike' and 'Patagonia', which reinforce but do not materially extend the schema's parameter meaning. This is a solid baseline-with-no-compensation-needed situation.

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 a specific verb ('Build a complete Brand Intelligence Profile') and a clear resource ('by searching ALL knowledge graphs for a specific brand'). It enumerates rich outputs such as trend footprint, competitive landscape, and cross-graph presence, and it is clearly differentiated from sibling tools like search_graph because it searches ALL graphs and returns aggregated intelligence.

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 says 'Use when the query is about a specific company or brand' and gives concrete example queries. It does not name alternatives or say when not to use this tool, so it stops short of a full usage heuristics with exclusions, but the target usage context is very clear.

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