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

Annotations already declare readOnly, openWorld, and idempotent behavior. The description adds value by disclosing concrete behavioral details: it analyzes public web data including robots.txt and OpenGraph metadata and returns a ranked list. This goes beyond the structured hints without contradicting them.

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 three tightly worded sentences that front-load the audience, output count, method, and necessary inputs. Every sentence contributes useful information with no fluff or repetition.

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?

For a moderately complex tool with nested objects and an async flag, the description adequately covers purpose, data sources, inputs, and output format. The async behavior is not explained in the description, but the schema documents it. The openWorld and readOnly annotations cover the remaining behavioral expectations, making the description sufficiently complete for tool selection and invocation.

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 coverage is 75% and includes clear descriptions for async, tech_stack_keywords, and top_customer_domains. The description reiterates the main inputs but adds little semantic detail beyond the schema. It does help by framing top_customer_domains and firmographic_filters as the core inputs, but does not fully compensate for undocumented nuances.

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 uses a specific verb ('discover') and clearly identifies the resource and output: 50 lookalike B2B accounts matching top customers' tech stacks and firmographics. It distinguishes the tool's ABM focus from generic account tools and is not a tautology.

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?

Provides clear context ('As a CMO', 'targeted ABM campaigns') and states the required inputs (top customer domains, firmographic filters). However, it does not explicitly mention when not to use this tool or name alternatives among the many sibling tools, so it lacks explicit exclusions.

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