Get price history and stats for a product over a trailing window (default 90 days). LMK-proprietary price intelligence.
| Name | Required | Description | Default |
|---|---|---|---|
| daysBack | No | ||
| idOrSlug | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| lowest | No | ||
| points | Yes | ||
| average | No | ||
| current | No | ||
| highest | No | ||
| history | Yes | ||
| isAtLowest | Yes | ||
| daysTracked | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds value by mentioning 'LMK-proprietary price intelligence' and the default 90-day window. However, it does not detail other behavioral aspects like response format or pagination, which are partially covered by the output schema.
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 with two sentences, front-loading the core purpose and adding a key differentiator (proprietary data) without any waste.
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?
Given low schema coverage (0%) and presence of an output schema, the description is incomplete. It lacks parameter explanations and usage context, which are critical for an agent to invoke the tool correctly. The output schema exists but does not compensate for missing parameter semantics.
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 description coverage is 0%, so the description should compensate. It only hints at 'daysBack' by mentioning a default 90 days but does not explain either parameter (e.g., what idOrSlug represents). This adds minimal value beyond the schema's type and constraints.
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 verb 'get' and the resource 'price history and stats for a product', which distinguishes it from sibling tools like get_product or compare_prices. The mention of 'LMK-proprietary price intelligence' further differentiates the tool's unique data source.
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. It only implies a trailing window default of 90 days but lacks explicit conditions, exclusions, or references to sibling tools.
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 are distinct, but 'fetch' and 'get_product' have overlapping functionality (both provide full detail and buying options), and 'search' vs 'search_catalog' could confuse agents about which to use for general search. Otherwise clear.
Predominantly uses 'get_' prefix for retrieval tools and 'search' for search tools, but 'fetch' and 'lookup_catalog' deviate slightly from the pattern, reducing consistency.
13 tools is well-scoped for a commerce server covering product discovery, pricing, deals, collections, and gift ideas without being overwhelming or sparse.
Covers key product research and price comparison workflows, but lacks tools for user interaction (e.g., wishlists, purchasing) or multi-product comparisons, which would enhance completeness.
