whats_newLatest changes seen by Faceabot's watch: standards (MCP, A2A, UCP), platforms, payments, paid placement in AI answers, stores letting agents in. Example: {"topic":"standards","days":7}
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | 1-365, default 14 | |
| topic | No | standards, platforms, commerce, payments, ads or access |
Changes observed during successful MCP inspections.
Input schema / properties / days / descriptionPrevious value: -"How many days back (1-365, default 14)"New value: +"1-365, default 14"Input schema / properties / topic / descriptionPrevious value: -"Optional filter: standards, platforms, commerce, payments, ads (paid placement in AI answers) or access (stores letting agents in)"New value: +"standards, platforms, commerce, payments, ads or access"Input schema / properties / topic / descriptionPrevious value: -"Optional filter: standards, platforms, commerce, payments or access (stores letting agents in)"New value: +"Optional filter: standards, platforms, commerce, payments, ads (paid placement in AI answers) or access (stores letting agents in)"Input schema / properties / topic / enumPrevious value: -[
- "standards",
- "platforms",
- "commerce",
- "payments",
- "access"
-]New value: +[
+ "standards",
+ "platforms",
+ "commerce",
+ "payments",
+ "ads",
+ "access"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is a safe, read-only, idempotent, closed-world query. The description adds useful source context ('Faceabot's watch') and domain scope, but it does not describe return shape, result limits, ordering, or freshness behavior beyond the days parameter.
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 compact sentences with purpose front-loaded and a concrete example. There is no redundant or filler text.
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 simple read-only query with full parameter documentation and rich annotations, the description supplies enough domain context and an example to call it correctly. It could be slightly stronger by stating what the returned 'changes' look like, but no output schema exists and the core behavior is clear.
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%, so the parameters are already documented. The description adds meaning beyond enum labels by clarifying what 'standards' covers and by hinting at what the ads/access categories refer to, plus an example invocation with days=7.
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 the resource clearly: latest changes in AI agent commerce, covering standards, platforms, payments, ads, and access. It gives concrete topical examples such as MCP/A2A/UCP and paid placement in AI answers. It does not explicitly differentiate from siblings, but none of the listed siblings overlap in 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 by 'Latest changes' and the example invocation, so an agent can infer this is for recent updates in a topic. However, there is no explicit when-to-use guidance, exclusions, or named alternatives.
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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