fetchFetch the full detail of a result returned by search, by its id (e.g. "product:"). Returns citeable product records (document text) for deep-research workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | A result id from `search`, e.g. "product:665f...". |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds value by disclosing that it returns 'citeable product records (document text)' and ties the id format to `product:<id>`, which goes beyond the annotations. It doesn't mention error cases or rate limits, but the read-only, non-destructive nature is well-covered.
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 two sentences long, front-loaded with the action ('Fetch the full detail'), and every sentence adds meaningful information. There is no fluff or repetition; it efficiently communicates the tool's purpose, input format, and output nature.
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?
This is a simple one-parameter, read-only tool with an output schema present. The description sufficiently explains the workflow context (after `search`, for deep-research), the id format, and the return value ('citeable product records'). It is complete for the tool's complexity and relies appropriately on the output schema for return details.
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?
The input schema already provides a complete description of the only parameter `id`, including an example ('product:665f...'). The tool description essentially repeats this information, adding no new parameter-specific semantics. With schema coverage at 100%, the baseline of 3 applies.
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 action (fetch full detail), the resource (a result returned by `search`), and the scope (by id). It explicitly distinguishes this from sibling tools like `search` by defining it as a follow-up, and even specifies the return type ('citeable product records'). This is a specific verb+resource+scope formulation.
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 provides clear usage context: use this tool after `search` to get full detail of a returned result id. It doesn't explicitly list alternatives or when-not-to-use scenarios, but the prerequisite of having a search result id is clear. The mention of 'deep-research workflows' adds contextual guidance.
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 target distinct data points (e.g., get_analytics_summary vs get_brand_score). However, 'search' and 'search_catalog' have overlapping functionality and could cause confusion, and 'fetch' is a helper tied to 'search', adding minor ambiguity.
The majority use consistent verb_noun snake_case (e.g., get_inventory, list_stores). Exceptions like 'fetch' and 'search' (without object) break the pattern, but they are few.
43 tools is excessive for a data-retrieval-only API. Many get_* and search_* tools could be consolidated (e.g., search_catalog, search_inventory, search_orders are similar). The large number will overwhelm an agent.
The tool set is entirely read-only (get, search, list, fetch). There are no create, update, or delete tools, which is a critical gap for managing e-commerce operations. Agents can only view data, not act on it.