log_click
Record that a human clicked an item after a receipt. Public.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | No | ||
| item_url | Yes | ||
| receipt_id | No |
Record that a human clicked an item after a receipt. Public.
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | No | ||
| item_url | Yes | ||
| receipt_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does disclose that this records a human-initiated click event and that it is public. It does not mention whether duplicate clicks are collapsed, any validation effects, or what the tool returns, though these are less critical for a simple logging action.
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?
Two short sentences with the core behavior front-loaded and no filler. 'Public.' at the end adds access context without bloating the description.
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?
The tool is a simple flat-parameter logger with no output schema, so the description is close to adequate, but it leaves the receipt_id relationship ambiguous and provides no parameter-level guidance. For a tool with no annotations, a bit more context about required fields or return behavior would make it fully complete.
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 description coverage is 0%, so the description must compensate, but it does not map the behavior to item_id, item_url, or receipt_id. The parameter names are somewhat self-explanatory, and the receipt context adds meaning, yet the required item_url vs optional receipt_id relationship is left unexplained.
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 names a specific action (record), a precise event (a human clicked an item), and a contextual qualifier (after a receipt). This distinguishes log_click from the sibling log_intent, which presumably records intent rather than an observed click.
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 phrase 'after a receipt' gives a clear contextual use case, and 'Public' signals an open call, but there is no explicit when-to-use versus alternatives or any exclusion. The distinction from log_intent is implied rather than stated.
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 cluster around pre-recommendation and human approval, so boundaries are blurry: commit_recommend, log_intent, whats_good_for, and trust_check all happen 'before recommending,' while request_approve and decide_approve differ mainly by who initiates. Descriptions help, but an agent could easily pick the wrong tool.
Most tools follow an imperative verb_noun snake_case pattern—log_click, spend_check, trust_check, ingest_listing—making the set predictable. nutrition_label and whats_good_for break that pattern, but the overall style is still consistent enough to navigate.
Ten tools fits the ideal 3-15 range and maps well to the server's trust-check, approval, logging, and listing-ingestion lifecycle. Each tool has a distinct role even if a few overlap conceptually.
The core workflow is well covered: policy checks, candidate lookup, logging, human approval, listing ingestion, and a nutrition stamp are all present. Missing observability and management endpoints like approval status/history or listing update/delete are workable gaps rather than dead ends.