walmart_productGet Walmart product details by item ID — title, brand, price, rating, ratings total, and image. Uses your BlueCart API key. Example: walmart_product({ item_id: "967006046", _apiKey: "your-bluecart-key" })
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | Your BlueCart API key (get one at trajectdata.com) — passed as the api_key query param | |
| item_id | Yes | Walmart item ID, e.g. "967006046" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and no destructive behavior. The description adds that it uses a BlueCart API key and lists the returned fields. No contradictions; the description complements the annotations by specifying data source and output structure.
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?
Two concise sentences plus a code example. Every word adds value: it states the action, outputs, prerequisite (API key), and usage pattern. No fluff, front-loaded with the main verb.
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 lookup tool with no output schema, the description adequately lists the returned fields. The annotations cover safety and idempotency. The parameter descriptions and example are complete. No gaps remain for an agent to misuse this tool.
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 a concrete example and notes that _apiKey is from BlueCart, including a hint about where to get it. This goes beyond the schema's basic description, providing usage context.
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 starts with 'Get Walmart product details by item ID', which is a specific verb and resource. It lists exact fields (title, brand, price, rating, ratings total, image). The name 'walmart_product' and inclusion among siblings like 'walmart_search' and 'walmart_reviews' clearly distinguishes it as a single-product detail tool.
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 says to use the item ID and API key, and provides an example. While it doesn't explicitly state when not to use it, the sibling tools (search, reviews) imply the context. A clear usage scenario is given: retrieving details by ID.
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.