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SetLuca

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by setluca

Approve a drafted reply and send it

luca_approve_and_send
Destructive

Approve a pending review-queue draft and dispatch it to the lead via their channel after human review. Pass confirm: true to send a real message.

Instructions

Approve a pending review-queue draft and dispatch it through the lead's channel. THIS SENDS A REAL MESSAGE TO A REAL PERSON — only call it after a human has reviewed and intends to send. Pass confirm: true to proceed. Optional finalBody replaces the draft text before sending. Example: { reviewQueueId, confirm: true }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirmNoThis tool has a real-world side effect: it messages real leads, books, moves, or cancels a call, changes a lead's consent, imports leads, writes to a connected calendar or CRM, sends workspace events to an outside URL, or re-triggers downstream automations. Pass confirm: true to proceed; the call is rejected without it.
finalBodyNoOptional edited body to send instead of the drafted text.
workspaceIdNoOptional Luca workspace id override.
reviewQueueIdYesThe review-queue item to approve.
workspaceSlugNoOptional Luca workspace slug override.
idempotencyKeyNoReuse the same key when retrying this exact approval.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.1/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already flag destructiveHint and openWorldHint, but the description adds critical context beyond them: the message goes to a REAL person, human review is mandatory, and confirm: true is a hard gate. This is exactly the kind of real-world impact and workflow constraint an agent needs before invoking a destructive send.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the action and the safety warning, then states the confirm requirement and an example. No filler. The example is short and reinforces the calling pattern; minor room to trim without losing meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be described. All six parameters are documented in the schema, and the description covers purpose, safety, and the confirm gate. It could note retry/idempotency behavior, but overall it is sufficient to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description restates confirm: true and that finalBody replaces the draft text, plus gives a usage example, but it adds little meaning beyond what the parameter descriptions already supply.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Approve a pending review-queue draft and dispatch it through the lead's channel.' The action is unambiguous. However, it does not differentiate itself from the sibling luca_review_queue_approve, leaving the agent to infer how approve-and-send differs from plain approve.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear precondition: only call after a human has reviewed and intends to send, and requires confirm: true. This is a strong when-to-use gate, but no alternative tool (e.g. luca_review_queue_approve or luca_conversations_send) is named for cases where the draft should not be sent.

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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