generate_email

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

With no annotations, the description carries full behavioral disclosure burden. It adds that the tool uses AI and bases output on contact info, company research, and profile, which provides some context. However, it does not disclose side effects (e.g., external API calls, potential latency, output format, or whether it requires prior research). This is adequate but not fully transparent for an AI-agent selecting the tool.

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 sentences, front-loaded with the core purpose, and no redundant information. The description is concise and earns each word.

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?

The tool is relatively simple (3 params, 1 required, no output schema). The description explains the main function and inputs but does not explicitly state the return format (draft email content) or any prerequisites (e.g., must have company research completed). It is largely complete for a generation tool, but a bit more detail on output and workflow would improve it.

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 coverage is 100% with each parameter having a description, so the baseline is 3. The description adds context by mentioning 'contact info, company research, and your profile,' which loosely relates to contact_id and job_posting_id, but it does not add specific meaning beyond the schema for tone or job_posting_id. Marginal added value.

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 uses a specific verb ('Generate') and resource ('outreach email'), clearly stating the tool creates a personalized AI-generated draft. This distinguishes it from sibling tools like schedule_email (sending) and schedule_followup (follow-up), as it explicitly says 'draft email'.

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 description implies the tool is for generating outreach emails based on contact info and company research, but it does not explicitly state when to use it versus alternatives (e.g., schedule_email, research_company). No exclusions or recommended workflow are provided, so usage guidance is only implied.

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