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inbox.send_reply

Destructive

Send an email reply through the user's Gmail. This emails a real person from the user's address. Only call after the user has seen the exact message body and explicitly approved sending it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesHTML body of the reply, exactly as approved by the user
item_idYesInbox item id
confirmedYesMust be true, and only after the user explicitly approved sending this exact message
draft_kindNoSet when the body came from inbox.draft_reply

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaNo

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already signal destructiveness (destructiveHint: true) and non-idempotency. The description adds real-world context ('emails a real person from the user's address') and reinforces the irreversible, consequential nature by linking it to explicit user approval. This goes beyond the structured annotations.

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?

Two sentences, both high-value. The first states the purpose and the second states the critical precondition. No fluff, front-loaded with the action and the approval constraint.

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

Completeness5/5

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

For a destructive send action, the description covers the approval requirement, the real-world impact, and the parameter linkage. Since an output schema exists, return values are already documented. Nothing essential is missing for an agent to call this safely and 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% and already includes descriptions for body and confirmed, including the requirement that confirmed must be true only after approval. The description reinforces this by saying the body must be exactly as approved, and the mention of draft_kind ('Set when the body came from inbox.draft_reply') adds useful cross-tool context not present in the schema.

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

Purpose5/5

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

The description states a specific verb (send) and resource (email reply via Gmail), and explicitly notes it emails a real person from the user's address, distinguishing it from drafting or applying suggestions. It clearly separates this from sibling tools like inbox.draft_reply.

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

Usage Guidelines5/5

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

Explicit when-to-use guidance: 'Only call after the user has seen the exact message body and explicitly approved sending it.' This is a strong guardrail that prevents premature or unauthorized sends. It also implies when not to call (without approval).

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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TDQS

A3.6/5.0
Disambiguation4/5

The tools are largely distinct due to the domain-prefixed naming (inbox, jobs, signals, etc.) and detailed descriptions. While there is some overlap among inbox actions like acknowledge, apply_suggestion, and decline, the descriptions clarify each behavior. The signals and recommendations sub-groups also have clear boundaries, making misselection unlikely.

Naming Consistency5/5

All tools follow a consistent pattern of domain.entity.action or domain.action (e.g., inbox.list, jobs.interviews.add, signals.recommendations.dismiss). This uniform camelCase-with-dots convention makes the set highly predictable and easy to navigate.

Tool Count2/5

At 55 tools, the server far exceeds the 25-tool threshold for 'too many' as per the calibration. While the broad domain of job search management justifies numerous operations, the count is still overwhelming and could overwhelm agents or cause selection errors. Several signal-related tools could potentially be consolidated without compromising functionality.

Completeness5/5

The tool set covers the full lifecycle of job applications: inbox management (list, get, draft, send), job tracking (add, update, archive, delete), interviews (add, update, delete), offers (create, update, accept, decline, negotiation), and company signals (track, pause, recommend, block). There are no obvious missing operations for the core workflows.

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