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draft_followup_email

Generate follow-up emails after meetings by referencing actual discussion points and action items. Choose from professional, casual, or concise tones to match your communication style.

Instructions

Draft a follow-up email based on a meeting. References actual discussion points.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
meeting_idYesThe meeting/conversation ID
recipient_nameNoName of the email recipient
toneNoEmail tone (default: professional)
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool drafts emails and references discussion points, but lacks critical details: whether it generates editable drafts or final sends, what permissions or data access are needed, how it handles missing information, or any rate limits. For a tool that likely accesses meeting data and creates content, this is insufficient transparency.

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?

The description is extremely concise at just two short sentences, with zero wasted words. It's front-loaded with the core purpose ('Draft a follow-up email based on a meeting') and adds only essential context ('References actual discussion points'). Every sentence earns its place by clarifying scope.

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

Completeness2/5

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

Given no annotations, no output schema, and a tool that creates content based on meeting data, the description is incomplete. It doesn't explain what the output looks like (e.g., email text, subject line), how meeting points are referenced, or behavioral constraints. For a 3-parameter tool with potential complexity in email generation, more context is needed.

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 the schema fully documents parameters (meeting_id, recipient_name, tone with enum). The description adds no additional parameter semantics beyond implying 'meeting_id' links to discussion points. With high schema coverage, the baseline is 3 even without param details in the description.

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?

The description clearly states the tool's purpose: 'Draft a follow-up email based on a meeting' with the specific action 'draft' and resource 'follow-up email'. It distinguishes from siblings like 'get_summary' or 'get_transcript' by focusing on email creation rather than information retrieval. However, it doesn't explicitly differentiate from all siblings (e.g., 'weekly_digest' might also involve email generation).

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a meeting transcript), exclusions (e.g., not for initial outreach), or comparisons with siblings like 'share_meeting' (which might involve email sharing). The phrase 'References actual discussion points' hints at context but doesn't constitute explicit usage guidelines.

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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