walmart_reviewsGet customer reviews for a Walmart product by item ID — title, body, rating, date, and verified-purchase flag. Uses your BlueCart API key. Example: walmart_reviews({ item_id: "967006046", _apiKey: "your-bluecart-key" })
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | Your BlueCart API key (get one at trajectdata.com) | |
| item_id | Yes | Walmart item ID, e.g. "967006046" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds no new behavioral traits beyond the API key requirement. It does not mention side effects, rate limits, or other operational details, so it provides marginal additional value.
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 extremely concise: a single sentence with a clear purpose, followed by a well-structured example. No redundant or unnecessary information.
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 simple, read-only tool with two parameters and no output schema, the description adequately covers the return fields and how to call it. The tool is low complexity, and the description is complete given the annotations.
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 described. The description adds an explicit example and a list of return fields (title, body, rating, date, verified-purchase flag), which provides semantic meaning beyond the schema descriptions alone.
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 gets customer reviews for a Walmart product, listing the data fields (title, body, rating, date, verified-purchase flag) and providing an example. It distinguishes itself from siblings like amazon_reviews or walmart_product.
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 does not provide any guidance on when to use this tool versus alternatives, such as walmart_product or walmart_search. It only demonstrates how to call it with an example, lacking when-to-use or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, e.g., Amazon and Walmart tools are platform-specific. A few overlapping tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are differentiated by clear usage guidance, so an agent can disambiguate with reasonable effort.
Tool names mix verbs and nouns with varying styles (e.g., ai_visibility_check, compare_entities, scan_competitor_ai_presence). There is no uniform pattern like verb_noun; some are descriptive phrases. The inconsistency is noticeable but not chaotic.
36 tools is too many for a server named 'Traject Ecommerce', as many tools cover unrelated domains (Polymarket, npm packages, SEC filings). The scope is excessively broad, making the server feel like a general-purpose plugin rather than a focused ecommerce toolset.
For an ecommerce-focused server, it only covers Amazon and Walmart product/search/reviews, missing major platforms and backend operations. The broader tool set is detailed but not ecommerce-specific, leaving obvious gaps for the intended purpose.