get_api_external_lastRetrieves the 20 most recently updated products along with their market prices. Billing per call: 0 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the read-only nature via 'Retrieves' and the zero-credit cost, which is useful. Yet it does not specify return format, ordering details beyond 'recently updated,' or any potential limitations, making it moderately transparent but not rich.
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 a single, clear sentence stating the core purpose, followed by a brief billing note. It is front-loaded, efficient, and contains no extraneous words.
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 the tool's simplicity (no parameters, no output schema), the description provides the essential facts: what it returns and its cost. It could elaborate on the output structure, but for a straightforward list retrieval, the current description is adequately complete.
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 tool has no parameters, so the schema is trivially covered. The description adds no parameter-specific information because none exist. According to the baseline rule for zero-parameter tools, a score of 4 is appropriate.
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 ('Retrieves') and resource ('20 most recently updated products') with a specific output scope ('along with their market prices'). This distinguishes it from siblings like getProducts or search by focusing on recency and the free billing aspect.
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 offers implied usage context: it is free and returns the latest products, so an agent would use it when these criteria are needed. However, it does not explicitly mention alternatives or when not to use it, leaving the comparison to siblings like search or getProducts implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes: search, paginated product listing, product-by-market pricing, price history, recent products, and market names. The main overlap is that product_of_market can optionally include price history while a dedicated history tool also exists, so an agent might occasionally choose between them.
All tool names share the get_api_external_ prefix, but the remaining portion is inconsistent: some use camelCase (getProducts, marketList), some use snake_case (get_db_info, product_of_market), and some use bare nouns (history, last, search). This mixed convention makes the tool surface feel less predictable.
Seven tools is well-scoped for a product barcode and pricing API. Each tool covers a meaningful query type without unnecessary bloat or redundancy.
The read-oriented surface covers searching, listing, product-market pricing, price history, recent updates, and market filter data. A minor gap is the lack of a dedicated product-details-by-barcode endpoint across all stores without requiring a store name, though search partially fills this.