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

With annotations already declaring readOnlyHint=true and openWorldHint=false, the safety profile is covered, so the bar is lower. The description adds genuinely useful return behavior: outputs are 'ranked' and contain 'pricing, platforms, and use cases' plus 'community engagement metrics', which matters because there is no output schema. It omits edge-case behavior (unknown products, zero results), but that is minor for a read-only lookup.

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?

Three sentences, no fluff — one states the function, one gives the trigger with examples, and one describes the return shape. Each sentence earns its place, and critical routing detail is front-loaded before the return-value detail.

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 moderate tool — 5 parameters, 1 required, read-only annotation — the combination of a fully documented schema and a behavior-rich description covers what an agent needs to select and call it correctly. The remaining gaps are the absence of any statement about how quantity limits apply (limit sends to 20, maximum by max related) and what happens when there are no results for an unknown product.

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 every parameter already carries its own description: product (with examples), limit (with default and range), audience, platforms, and pricingType (with enums). The description adds only a small layer of meaning by naming pricing/platforms as return fields, which loosely explains why the filters exist — but the schema mostly does the heavy lifting.

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 opens with a specific verb and resource: 'Finds alternatives and competitors to a specific software product, tool, app, or service.' Concrete examples ('alternative to Notion', 'something like Figma but free') disambiguate it from siblings like peerpush_find_product (locating the product itself) and peerpush_compare, so an agent can tell them apart without opening the 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?

The description explicitly names the trigger conditions — 'Relevant when the user asks for an alternative, replacement, or competitor to a product' — with concrete natural-language examples. It gets a 4 rather than a 5 because it does not name sibling tools or give when-not conditions (e.g., 'for comparing two specific products use peerpush_compare'), so the agent must infer the exclusions from the sibling list.

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