Finds a professional email address using a company domain, first name, and last name to support outreach and contact enrichment.
Finds a professional email address using company domain, first name, and last name.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | ||
| last_name | Yes | ||
| first_name | Yes |
Changes observed during successful MCP inspections.
v1.0.1Input schema / properties / domain / descriptionRemoved value: -"Company domain, e.g. openai.com"Input schema / properties / first_name / descriptionRemoved value: -"Person's first name."Input schema / properties / last_name / descriptionRemoved value: -"Person's last name."v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It says only that the tool finds an email and does not explain success rates, whether results are guessed versus verified, failure behavior, permissions, or output characteristics.
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 a single efficient sentence with no wasted language and is front-loaded with the core purpose and required inputs. Every part 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 annotations, no output schema, and no parameter descriptions, the context is very thin. The description covers the basic action and inputs but omits return behavior, failure modes, and sibling differentiation, which are necessary for reliable invocation.
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 0%, so the description must compensate for undocumented parameters. It names company domain, first name, and last name, but does not specify accepted domain formats, name handling, or what happens with missing inputs, adding only minimal meaning beyond the schema.
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 clear verb and resource: finds a professional email address. It also names the three required inputs, so the agent can distinguish it from generic search or validation tools. However, it does not explicitly differentiate it from close siblings like run_linkedin_email or run_email_enrichment.
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 required inputs imply that the tool is used when a company domain, first name, and last name are known, but the description gives no explicit when-to-use guidance. It also does not mention alternatives such as run_linkedin_email, run_email_enrichment, or run_email_validation, leaving the agent to infer routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.