get_api_statusCheck whether the Pricewatcha public API is available. Returns health and discovery metadata. On failure, returns a structured error object with fields error.code, error.message, error.http_status, error.retry_recommended, and error.retry_after_seconds.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| api | Yes | ||
| health | Yes | ||
| base_url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context about failure responses, detailing the structured error object fields, which goes beyond the annotations. No contradictions with annotations.
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, front-loaded with the primary purpose, then covers both success and failure outcomes. No extraneous information, every sentence earns its place.
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 is simple (zero parameters, read-only, idempotent) and an output schema exists (per context), the description covers the essential behavioral aspects including error handling. It is sufficiently complete for an agent to use effectively.
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?
There are no parameters, so the schema coverage is 100%. The description does not need to add parameter details, and the baseline for zero parameters is 4. The description is adequate.
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 tool checks whether the Pricewatcha public API is available, with a specific verb and resource. It distinguishes from sibling tools like get_job_status or search_products by focusing on API health.
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 implies usage for checking API availability but does not explicitly specify when to use or avoid this tool compared to alternatives. There is no mention of prerequisites or exclusions, leaving the agent to infer based on tool name and purpose.
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 clearly maps to a distinct resource and action: price alert CRUD verbs (create/get/list/update/delete) are unambiguous, and get_api_status vs get_job_status are separated by scope (service health vs async job polling). track_product, search_products, get_product, and get_price_history each address a different part of the product workflow with no meaningful overlap.
All 11 tools follow a consistent verb_noun snake_case pattern: create_price_alert, get_price_history, track_product, list_price_alerts, and so on. Verbs are precise and nouns are stable across the set, making the API surface predictable.
At 11 tools, the server is well-scoped for its purpose: alert management, product search, price history, tracking, and status checks each earn their place. No tool feels redundant or excessive for a price-intelligence domain.
The price alert lifecycle is fully covered with create/get/list/update/delete, and product search/tracking/history are solid. A minor gap is the lack of a way to list or stop all tracked products/jobs besides polling a known job_id, but this can be worked around and core workflows are complete.