Draft an AI outreach email

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

With no annotations provided, the description bears full transparency responsibility. It discloses the underlying endpoint (POST /api/v1/outreach/draft), the model used (claude-sonnet-4-6), and return fields (subject, body, confidence score, reasoning), which is valuable. However, it does not explicitly state whether the draft persists, whether the POST is read-only or state-changing, or any potential side effects like quota consumption, leaving key behavioral traits undisclosed.

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 four concise sentences, front-loaded with the primary purpose first. Every sentence adds distinct value: purpose, implementation detail (endpoint), model specification, and return payload. There is no redundant phrasing or filler, making it highly skimmable.

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 of moderate complexity, the description covers the essential aspects: what it does, how it works (endpoint/model), and what it returns. The lack of an output schema is mitigated by explicitly listing the return fields. Minor gaps exist around edge cases (e.g., invalid guestId), but overall this is adequately complete for an independently useful tool.

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 100%, with each parameter already having a meaningful description (e.g., 'Guest ID to draft outreach for'). The tool description adds no extra parameter-level meaning beyond what the schema provides, so the baseline of 3 applies without any significant upward adjustment.

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 opens with 'Generate an AI outreach email draft for a guest,' clearly stating a specific verb, resource, and audience. It further distinguishes itself from sibling tools (list_guests, add_guest, etc.) by focusing on email drafting rather than guest CRUD or analytics, making its purpose unmistakable.

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?

Usage context is implied by the tool name and the verb 'Generate'—an agent can infer when to invoke it, but there is no explicit when-to-use or when-to-avoid statement. No alternatives are mentioned, nor are prerequisites like 'guest must exist in the system' or 'use after list_guests to obtain guestId,' leaving room for misinterpretation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.