log_intent
Log that you intend to recommend or act. Needs a write key. Returns receipt_id.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| action | Yes | ||
| result | No | ||
| policy_id | No | ||
| target_url | Yes |
Log that you intend to recommend or act. Needs a write key. Returns receipt_id.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| action | Yes | ||
| result | No | ||
| policy_id | No | ||
| target_url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It meaningfully adds that a write key is required and that a receipt_id is returned, which informs authentication and success behavior. However, it does not disclose side effects, idempotency, failure modes, or whether the logged intent is externally visible.
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 exceptionally concise: three short sentences deliver purpose, a key prerequisite, and the expected return value. Every sentence earns its place, and the main purpose is front-loaded. No filler or redundant wording is present.
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 five parameters, no annotations, and no output schema, the description is too sparse. It covers the write key and return receipt, but omits parameter meanings, when to call, and behavioral details needed for reliable agent selection and invocation. An agent would likely need to guess at several important fields.
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 for the five undocumented parameters. It does not explain query, action, target_url, result, or policy_id, leaving the agent to infer their meanings purely from names. This is a critical gap for correct invocation.
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 action and resource: 'Log that you intend to recommend or act.' This clearly identifies the tool's purpose and distinguishes it from sibling log_click by emphasizing intent rather than the click event. However, 'recommend or act' is somewhat broad and does not explicitly contrast with other sibling tools.
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?
No guidance is given about when to use this tool versus alternatives. The description implies it should be used before recommending or acting, but it does not state when not to use it, mention sibling alternatives, or clarify conditions. The write-key requirement is a prerequisite, not usage context.
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.