decide_approve
Human tap. Public so the desk can approve without the org key.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| decision | Yes | ||
| approval_id | Yes |
Human tap. Public so the desk can approve without the org key.
| Name | Required | Description | Default |
|---|---|---|---|
| decision | Yes | ||
| approval_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It does reveal that the tool is public and human-invoked ('Human tap'), which is useful, but it does not disclose side effects, whether the decision changes state, reversibility, or any safety implications.
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 short but cryptic. 'Human tap' is jargon rather than clear prose, and the two fragments do not provide enough substance. This is under-specification, not efficient conciseness.
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 2 required parameters, no output schema, and no annotations, the description is severely incomplete. It does not clarify the intended operation, the meaning of the decision values, what happens after approval, or what response to expect.
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 adds no meaning to either parameter. It does not mention approval_id, decision, or the significance of the approved/denied enum, leaving the agent to infer semantics solely from parameter names.
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 'Human tap. Public so the desk can approve without the org key.' is vague about what the tool actually does. It references approving but does not clearly state that this tool decides whether an approval request is approved or denied, and it relies heavily on the tool name and parameter enum for meaning.
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 minimal context about a desk user approving without an org key, but it provides no explicit guidance on when to use this tool versus siblings like request_approve. There is no when-to-use or when-not-to-use direction.
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.