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

Adds substantial context beyond annotations: 5 prepaid credits per successful call (cost/rate-limit info), output shape (JSON search_results with item arrays), and the important caveat that the MCP layer returns a compact view rather than raw results. Annotations already cover readOnly/openWorld, so this is strong additive disclosure.

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, front-loaded with the core purpose, followed by output detail and pricing. No filler, though the 'Walmart product search API for AI agents' framing restates the name somewhat.

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?

No output schema exists, and the description covers return structure, cost, and the compact-view caveat; combined with a fully documented 2-param schema, an agent has enough to call correctly. Missing only explicit pagination/error semantics and when to prefer search_shopping.

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 100% with both parameters fully documented (query, results with default 10 and max 20), so the description need not compensate. 'search URL' phrasing is slightly loose versus the actual 'query' string parameter, adding no real semantic value. Baseline 3.

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+resource ('retrieve listings from a Walmart search URL') and enumerates what comes back (titles, prices, ratings, availability, seller details). Sibling set includes search_amazon, search_ebay, search_shopping — the Walmart scope makes it trivially distinguishable.

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?

Mentions 'for ecommerce research', implying a research use case, but gives no explicit when-to-use/when-not guidance relative to search_shopping or search_amazon. An agent gets a hint of context but must infer the routing decision itself.

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