email-verifierChecks email addresses: syntax, domain, and whether the domain publishes mail exchangers. Where mailbox-level existence cannot be established, the field is null rather than a guess. — $0.02/call, x402 (USDC on base).
| Name | Required | Description | Default |
|---|---|---|---|
| emails | Yes | Email addresses to verify. | |
| maxConcurrency | No | How many emails to check in parallel. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and destructiveHint. The description adds important behavioral context: the null-when-uncertain policy for mailbox existence, which prevents false certainty, and the pricing model per call. This goes beyond what annotations provide.
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 two sentences with no wasted words. It packs the core check scope, the null behavior caveat, and pricing into a compact format, every sentence earning 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?
For a simple tool with no output schema, the description provides enough context: what is checked and the null policy. However, it doesn't describe the output shape (e.g., per-email result objects), but the null behavior hints at it. Still, it is sufficiently complete for an agent to invoke 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 coverage is 100% with descriptions for both 'emails' and 'maxConcurrency'. The description does not add parameter-specific semantics beyond the schema, so the baseline of 3 is appropriate.
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 clearly states the verb 'checks' and specifies the resource and scope: syntax, domain, and mail exchangers. It distinguishes this tool from siblings focused on companies and signals, 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?
The description implies usage context by listing what it checks and the null behavior, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. This is clear context without explicit guidance.
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 have clearly distinct purposes, but some overlap exists between company-lookup and company-registry-enricher, and between individual signal tools and composite rollups. The descriptive names help differentiate, but the boundary between one-off screenings and alert/rollup tools requires careful reading.
Tool names follow a consistent pattern of hyphenated lowercase nouns (e.g., company-lookup, funding-alert, sanctions-screening). The one exception, pricing_info, uses an underscore, creating a minor deviation from the otherwise uniform naming style.
With 20 tools, the server is on the higher end of typical scope but justified for a comprehensive company intelligence bundle. Each tool covers a distinct or complementary aspect of company research, so the count feels purposeful rather than bloated.
The toolset covers company lookup, registry, hiring, funding, sanctions, litigation, patents, contacts, new company detection, and email verification—a broad and well-rounded surface for due diligence and sales intelligence. Composite tools like intent-signal-aggregator and lead-list-qualifier tie these together effectively, leaving no major dead ends.