pricewatcha

Track product

track_product

Submit a public product URL for price tracking. Waits up to ~25s server-side; fast shops return status "completed" with product in one call. Slow jobs return status "running" with job_id — poll get_job_status. On failure, returns a structured error object with fields error.code, error.message, error.http_status, error.retry_recommended, and error.retry_after_seconds.

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	Public product page URL from a supported shop
`watch`	No	If true (API key required), enroll the product for continuous scheduler updates.
`api_key`	No	Optional API key (pwk_live_...). Required for watch/refresh.
`refresh`	No	If true (API key required), force a re-scrape even if the URL is already in the catalog.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`hint`	No
`error`	No
`job_id`	Yes
`status`	Yes
`product`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 7, 2026

addedInput schema / properties / api_key

Added value: +{
+  "description": "Optional API key (pwk_live_...). Required for watch/refresh.",
+  "minLength": 1,
+  "type": "string"
+}

addedInput schema / properties / refresh

Added value: +{
+  "description": "If true (API key required), force a re-scrape even if the URL is already in the catalog.",
+  "type": "boolean"
+}

addedInput schema / properties / watch

Added value: +{
+  "description": "If true (API key required), enroll the product for continuous scheduler updates.",
+  "type": "boolean"
+}

First observedJun 7, 2026

TDQS

A4.3/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes well beyond annotations by disclosing server-side wait time (~25s), two possible status paths, the job_id polling mechanism, and the full structured error object shape. Since annotations are all false and provide little safety information, this behavioral detail is especially valuable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three focused sentences: the first states the action, the second covers the normal and slow paths, and the third covers errors. There is no filler or repetition, and key behavioral details are front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully covers the main invocation flow, timeout behavior, polling fallback, and error handling. Since an output schema exists, return value details are not the description's burden, and the tool's complexity is well supported by the provided text.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-level detail beyond the schema; it only mentions job_id, which is part of the response, not a parameter. The schema already documents url, watch, api_key, and refresh adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: submit a public product URL for price tracking. It clearly explains the action and result, but it does not explicitly contrast track_product with similar siblings like watch_product or get_product, so sibling differentiation is left implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for the async flow: fast shops return completed, slow jobs return running with a job_id and instruct the agent to poll get_job_status. It does not explicitly state when to choose this tool over watch_product or when not to use it, but the polling guidance is actionable.

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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