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

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond this: it describes the parallel fan-out across multiple sources, the soft-failure of the USPTO PatentsView API (sunset May 2025), the fallback mechanism (GDELT→GNews), and the specific data fields returned. This is strong disclosure beyond annotations, though a few more details about rate limits or error handling could push it higher.

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 efficiently packed with essential information, starting with example queries to quickly convey purpose. Every sentence earns its place: usage preference, source list, output details, limitations, and fallback behavior. There is no extraneous text, but also no omitted critical details. The structure is front-loaded and logical.

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?

Despite the tool's complexity (aggregating multiple data sources) and the absence of an output schema, the description thoroughly explains what the agent can expect: specific fields like cik, company_name, recent_filings with URIs, fundamentals, patents, news, and LEI. It also notes limitations (name not supported, USPTO API sunset). With only two simple parameters, this level of detail adequately completes the picture for an agent.

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 input schema already provides a description for both parameters (type and value), achieving 100% schema description coverage. The description adds further value by explaining that 'value' can be a ticker or zero-padded CIK, gives examples ('AAPL', '0000320193'), and clarifies that names are not supported. It also notes that 'type' currently only supports 'company' and that other entity types are coming soon. This additional context enhances the schema's semantic clarity.

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 clearly states the tool's purpose: generating a full cross-source profile of a US public company. It provides specific example queries ('Tell me about X', 'research Acme') and lists the resources it aggregates (SEC EDGAR, XBRL, USPTO, news, GLEIF). It also distinguishes itself from sibling tools like resolve_entity and compare_entities by noting that names are not supported here and that resolve_entity should be used first if only a name is available.

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 description gives explicit guidance on when to use this tool: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also states when not to use it (if only a name is available) and provides a clear alternative: 'use resolve_entity first.' This meets the highest standard for usage guidelines.

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