Bulk-verify email addresses: syntax, MX records, SMTP deliverability, disposable-domain and role-based detection. Use before sending outreach so you only contact addresses that actually exist.
| Name | Required | Description | Default |
|---|---|---|---|
| emails | Yes | Email addresses to verify | |
| checkSmtp | No | Run SMTP deliverability probe (default true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description carries the full burden. It discloses the key behaviors: bulk operation, syntax checking, MX record lookup, SMTP probing, and disposable/role-based detection. It does not mention rate limits, response format, or potential latency, but for a verification tool, the behavioral profile is reasonably transparent.
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 with zero wasted words. The first sentence states what the tool does, the second provides usage context. Information is front-loaded and scannable.
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 description covers purpose and usage but lacks information about the tool's output (return values) and any limitations (e.g., email volume limits). Without an output schema, the description should hint at what the agent receives, which it does not. The tool is simple, so the gap is moderate.
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 baseline is 3. The description does not add new meaning beyond the schema; it reiterates the checks but does not elaborate on parameter usage or constraints. The parameter semantics are adequately covered by 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 uses specific verbs and resources: 'Bulk-verify email addresses' and lists the types of checks (syntax, MX, SMTP, disposable, role-based). It clearly differentiates from sibling tools, none of which perform email verification.
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 explicitly states when to use: 'before sending outreach' and the goal 'so you only contact addresses that actually exist.' It does not mention when not to use or alternatives, but the context is clear and sufficient for the tool's standalone purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
