verify_emailCheck whether an email address exists and is safe to send to, before it bounces. Returns a deliverability verdict (deliverable, undeliverable, or unknown) without sending anything. Spends 1 credit, refunded if no verdict can be reached.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The email address to verify, e.g. jordan@acme.com. |
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?
Beyond annotations, the description reveals important behaviors: it spends 1 credit, refunds if no verdict can be reached, and sends nothing. This adds value beyond readOnlyHint being false and clarifies side effects and cost, which are essential for an agent to use the tool correctly.
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 three concise sentences, each earning its place: purpose, outcome, and side effect. It is front-loaded with the core question and avoids unnecessary filler.
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 single-parameter tool with no output schema, the description is complete. It explains the verdict values, the credit cost, the refund behavior, and the fact that nothing is sent. An agent has enough information to decide whether and how to invoke this tool.
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 only parameter is already fully described in the schema with a clear example. The tool description does not add extra meaning beyond what the schema provides, so 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 states a specific verb ('Check') and resource ('whether an email address exists and is safe to send to'), and clarifies its unique role with 'before it bounces' and 'without sending anything.' This distinguishes it clearly from sibling tools like reverse_email_lookup, which do not focus on deliverability.
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 provides clear context for when to use the tool: before sending an email to avoid bounces. It does not explicitly name alternatives or exclusion criteria, but the context is strong enough for an agent to infer the appropriate use case.
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 distinct purposes, but pairs like company_search/lookup_company and find_linkedin_profile/lookup_linkedin_profile could cause selection errors. The free-search versus paid-unlock flow (people_search/find_people/reveal_profile) is well-differentiated by detailed descriptions.
Naming mixes verb_noun patterns (find_people, fetch_email) with noun_verb patterns (company_search, people_search), and uses overlapping verbs like find, lookup, search, and fetch. The pattern is readable but not consistently predictable.
With 12 tools, the set is well-scoped for a people search and email enrichment service. Each tool covers a distinct operation, from free search to paid profile unlock, email verification, and credit checking, without unnecessary bloat.
The toolset covers the core lifecycle: free search, paid profile unlock, email fetch, verification, and reverse lookup. Minor gaps exist, such as not being able to fetch an email later for a profile unlocked via reveal_profile unless include_email is set initially, but agents can work around these.