walmart_searchSearch Walmart products by keyword — returns position, title, item ID, price, rating, and ratings total. Uses your BlueCart API key. Example: walmart_search({ search_term: "coffee maker", _apiKey: "your-bluecart-key" })
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | Your BlueCart API key (get one at trajectdata.com) | |
| search_term | Yes | Search query, e.g. "coffee maker" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description confirms read-only nature and adds return fields. No contradictions. Adds some value beyond annotations but not extensive.
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 an example. Front-loaded with purpose and return fields. No redundant 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?
With no output schema, description adequately explains return fields. Covers API key requirement and provides example. Missing pagination details but acceptable for a search 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%, so baseline 3. Description adds context: example usage and that API key is from BlueCart, providing extra meaning beyond schema properties.
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?
Description uses specific verb 'search' and resource 'Walmart products', lists return fields (position, title, item ID, price, rating, ratings total), and distinguishes from siblings like walmart_product and walmart_reviews.
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 an example and mentions API key, implicitly suggesting when to use (search by keyword). Does not explicitly state when not to use or differentiate from alternatives, but context from sibling names helps.
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.