find_decision_maker_emailFind a likely decision maker and verified email for a domain. Uses Potarix Enricher API credits.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Company domain, such as 'stripe.com'. | |
| category | Yes | Decision maker category, such as 'ceo', 'sales', or 'operations'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnly/idempotent/openWorld signals, and the description adds valuable context about API credit consumption (Potarix Enricher API credits) and the verification aspect. It doesn't cover edge cases like not-found results, but the added cost details go beyond the annotations.
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 extremely concise, with two sentences that front-load the core purpose and then mention the API credit usage. No redundancy or wasted words.
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 two-parameter tool with strong annotations and no output schema, the description adequately explains the key behavior and cost. It could specify the exact return format, but the essential information—what is found and at what cost—is present.
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?
The input schema already provides thorough descriptions for both parameters—domain ('stripe.com') and category ('ceo', 'sales', 'operations')—so the description adds no extra parameter-specific meaning. With 100% schema coverage, the baseline score 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 tool's function: to find a likely decision maker and verified email for a domain. This distinguishes it from sibling tools like find_person_email (specific person) and find_company_emails (company email list).
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 intended usage is implied by the name and description (search by domain and decision maker category), but there is no explicit guidance on when to choose this over alternatives like find_person_email or find_company_emails.
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.
The find_* tools all relate to email discovery but differ by input (domain, LinkedIn URL, person+company), and the descriptions clarify these distinctions. However, find_all overlaps with several individual tools, and find_company_emails vs find_decision_maker_email could cause slight confusion despite their specific purposes.
All tool names follow a consistent snake_case verb_noun pattern (check_balance, find_company_emails, lookup_company_website, etc.). Even the more generic find_all fits the pattern. No mixed conventions or inconsistent casing.
With 9 tools, the set is well-scoped for an email enrichment service with billing support. It covers core enrichment operations and account management without unnecessary bloat.
The domain is well covered: website lookup, various email finding methods, and a combined find_all. Billing is handled with balance, checkout, and top-up. A minor gap is lack of API key management (only count is shown), but it doesn't cause dead ends.