product_intelExtract product price, availability, add-to-cart, and commerce risk signals. Price: $0.15 USDC. Example: GET /v1/product-intel?url=https://example.com
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering safety and external access. The description adds the monetary cost ('Price: $0.15 USDC') and an example endpoint, which are valuable behavioral details. No contradictions detected.
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, consisting of two sentences. It is front-loaded with the primary purpose, followed by cost and an example. Every sentence adds necessary information without 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?
For a tool with one parameter and strong annotations, the description covers purpose, cost, and an example sufficiently. It does not explicitly describe the output format, but the named extracted signals ('price, availability, add-to-cart, and commerce risk signals') imply the return content. Minor gaps include failure behavior or response structure, but overall it's adequate.
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 schema only defines a 'url' with format 'uri' and no description. The description compensates slightly by showing an example query string ('?url=https://example.com'), which clarifies the parameter usage. However, it does not explain accepted URL types, encoding, or other constraints, so it only partially compensates for the 0% schema description coverage.
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 states a specific verb+resource: 'Extract product price, availability, add-to-cart, and commerce risk signals.' It clearly identifies what the tool does and distinguishes it from sibling preflight/discovery tools by focusing on product intelligence. The example endpoint reinforces the purpose.
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?
Usage is implied: an agent would use this when needing product price, availability, or commerce risk signals from a URL. However, there is no explicit mention of when to use this over alternatives, nor any exclusions. The example provides a basic usage pattern but not contextual guidance.
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.
Several tools overlap significantly: preflight, premium_preflight, and batch_preflight all perform URL preflight with varying price/features, and access_check appears to be a subset of preflight. This creates ambiguity in selecting the right tool for a task.
Most names use snake_case, but the pattern varies: some end in _check, _preflight, _discovery, or _intel, and preflight is a single word without a verb. The naming is readable but not uniformly consistent.
With 7 tools, the count is well within the ideal 3-15 range and appropriate for a URL preflight service that covers different access levels, batch processing, and specialized checks.
The tool set covers core preflight, access, commerce, API discovery, change detection, and batch operations. Minor gaps exist (e.g., no dedicated raw fetch or history endpoint), but they are not critical and can be worked around.