commit_recommend
Call BEFORE every recommend. Runs trust_check, logs pass/fail, returns speak + receipt. Needs a write key.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| action | Yes | ||
| policy_id | No | ||
| target_url | Yes |
Call BEFORE every recommend. Runs trust_check, logs pass/fail, returns speak + receipt. Needs a write key.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| action | Yes | ||
| policy_id | No | ||
| target_url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose side effects and requirements. It does: it runs trust_check, logs pass/fail, returns speak + receipt, and needs a write key. This covers the operation's dependencies and logging side effect, though it does not explain failure behavior or what speak/receipt contain.
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 deliver the core usage rule, behavior, outputs, and a prerequisite. Every clause earns its place and the most important guidance is front-loaded. No filler or repetition.
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 description gives a useful workflow hint but is incomplete for a tool with four undocumented parameters and no output schema. It names return values 'speak + receipt' without explaining their meaning, gives no parameter guidance, and does not clarify failure or edge-case behavior.
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% and the description provides no explanation of query, action, policy_id, or target_url. It only mentions needing a write key, which is not mapped to any parameter. The description fails to compensate for the fully undocumented schema.
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 trigger ('Call BEFORE every recommend') and a concrete behavior: it runs trust_check, logs pass/fail, and returns speak + receipt. This distinguishes it from the sibling trust_check because commit_recommend adds logging and returns extra artifacts, though 'recommend' itself is not in the sibling list and 'speak + receipt' are left undefined.
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 description gives explicit timing guidance: 'Call BEFORE every recommend.' It also mentions the required write key prerequisite. It does not explicitly say when not to use it or name alternatives, but the 'before every recommend' rule gives a clear 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.