generate_email_from_urlScrape a URL (e.g. a launch blog post or product page) and generate an email draft from its content. Returns subject, preview text, and HTML body.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public URL to scrape and turn into an email | |
| apiKey | No | Send16 API key (overrides SEND16_API_KEY env var) | |
| instructions | No | Additional guidance; e.g. 'announcement style, focus on the new feature' |
Changes observed during successful MCP inspections.
Input schema / properties / instructions / descriptionPrevious value: -"Additional guidance — e.g. 'announcement style, focus on the new feature'"New value: +"Additional guidance; e.g. 'announcement style, focus on the new feature'"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior, so the safety profile is covered. The description adds meaningful context beyond that: it reveals the tool scrapes external web content and produces a draft (not a sent email), plus the exact return fields. It omits auth/rate-limit notes, though the apiKey parameter hints at these.
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?
Two tight sentences with zero filler; the core action is front-loaded and the return values follow. Every clause earns its place.
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?
With no output schema, the description usefully enumerates the return values (subject, preview text, HTML body), which an agent would otherwise lack. It is slightly thin on how 'instructions' shapes the generation, but overall it equips the agent to call the tool correctly.
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%, so the schema already documents url, apiKey, and instructions in detail. The description adds no additional parameter-level meaning (it never mentions instructions or the API key override), so the baseline 3 for schema-documented parameters applies.
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 states a specific two-step verb (scrape a URL, generate an email draft) and the resource (email draft), which is clearly distinct from siblings like design_email_template or render_email_content. It never explicitly names an alternative tool, so it stops short of the top score for sibling differentiation.
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?
The parenthetical examples ('a launch blog post or product page') imply the intended use case, giving some context for when to reach for this tool. However, there is no explicit when-to-use/when-not guidance and no reference to alternative drafting or templating tools, so usage is only implied.
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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