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confirm-ai

confirm-mcp

by confirm-ai

Request human approval

request_approval

Get human approval before executing sensitive or irreversible actions. Blocks for a decision and returns the approved payload to use exactly, or aborts on rejection/expiry.

Instructions

Pause and ask a human to approve a sensitive or irreversible action before you perform it (refunds, deletes, deploys, outbound messages, spending money). Blocks until the human approves, rejects, or the wait elapses. If APPROVED, use the returned effectivePayload exactly (the human may have edited it). If REJECTED or EXPIRED, do not perform the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notifyYesApprover email address, or 'group:<key>' to escalate to a group.
payloadNoThe exact action as structured data. The human can edit this.
summaryYesOne line the human reads, e.g. 'Refund $10,000 to customer #4821'.
reasoningNoWhy you want to do this.
agent_nameNoYour name, shown to the approver.
ttl_minutesNoHow long the request stays valid.
wait_minutesNoHow long to block before returning pending (default 15).
recent_actionsNoYour recent steps, for context.
Behavior5/5

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

With no annotations, the description fully discloses key behaviors: it blocks until approved/rejected/expired, and instructs to use the returned effectivePayload exactly if approved, since the human may edit it. This is critical operational context beyond schema.

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

Conciseness5/5

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

Three sentences, front-loaded with the tool's purpose, no wasted words. Each sentence contributes essential information.

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?

Despite no output schema, the description covers outcomes (approved/rejected/expired) and resulting action. The 8 parameters are all documented in the schema, and the description supplements with enough behavioral context for an agent to invoke correctly.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful operational instruction about payload handling (use returned effectivePayload exactly), which goes beyond the schema's 'human can edit this' and clarifies the payload's role.

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?

Description clearly states it requests human approval for sensitive actions, with specific examples (refunds, deletes, deploys, outbound messages, spending money). However, it does not explicitly reference sibling tool check_approval, so sibling differentiation is implicit rather than explicit.

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?

Provides clear context: use before performing sensitive or irreversible actions. It does not explicitly state when not to use or mention alternatives like check_approval, so it falls short of full guidance.

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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