get_price_history
Price-stability signal, latest verified snapshot, last observed change and derived trend for one product.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug. |
Price-stability signal, latest verified snapshot, last observed change and derived trend for one product.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It lists the data returned (stability signal, snapshot, change, trend), which is adequate for a read-only query. However, it does not disclose whether it requires any special permissions or if there are rate limits.
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?
A single concise sentence of 14 words that front-loads the key output ('Price-stability signal'). No wasted words, though it could be slightly more structured.
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 only 1 parameter and no output schema, the description adequately lists the types of data returned (stability signal, snapshot, change, trend). It is sufficient for an agent to understand the tool's output.
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% (the only parameter 'slug' is described in schema). The description does not add meaning beyond the schema's 'Product slug.' Baseline 3 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 explicitly states what the tool retrieves: price-stability signal, latest verified snapshot, last observed change, and derived trend for a single product. The purpose is distinct from sibling tools like get_pricing or get_cost_guide.
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?
No guidance on when to use this tool versus alternatives. It only mentions it is for one product, but does not specify when to prefer it over get_pricing or other pricing 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.
Each tool has a clearly distinct purpose: from TCO calculation to comparison, discovery, LLM cost estimation, negotiation guides, hidden costs, positioning, price history, and full pricing records. No two tools overlap significantly; they complement each other.
All tool names follow a consistent verb_noun pattern with lowercase and underscores, primarily using 'get_' for five tools and other specific verbs like 'calculate', 'compare', 'discover', 'estimate'.
With 9 tools, the server is well-scoped for a pricing analysis domain, covering essential queries without being overwhelming or sparse.
The tool surface comprehensively covers the pricing domain: discovery, comparison, detailed pricing, hidden costs, TCO, price history, positioning, and negotiation advice. No obvious gaps are present for the intended use case.