reverse_email_lookupTurn a professional email address into the real person behind it and their LinkedIn profile. Best for work emails; free-mailbox addresses often have no public profile to match. Spends 13 credits (this is the single most expensive lookup), refunded automatically if no match is found.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The email address to identify, 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?
The description adds significant behavioral detail beyond annotations: 'Spends 13 credits (this is the single most expensive lookup), refunded automatically if no match is found.' This cost and refund behavior is valuable context not visible in the annotations, and it is consistent with idempotentHint=false and readOnlyHint=false.
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?
Three tightly written sentences with no filler. The main purpose is front-loaded, followed by usage qualification and the cost/refund detail. Every sentence 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?
For a one-parameter tool with no output schema, the description is complete: it states what the tool returns ('real person behind it and their LinkedIn profile'), when it works, and what happens on failure (automatic refund). An agent has enough context to invoke it 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?
The schema already covers the email parameter fully with an example. The description adds extra semantics by calling it a 'professional email address' and explaining the work-email/free-mailbox distinction, which helps the agent choose valid inputs and set expectations.
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 a specific verb ('Turn') and states the resource ('professional email address') and the result ('real person behind it and their LinkedIn profile'), making the tool's purpose concrete. However, it does not explicitly distinguish itself from overlapping siblings like find_linkedin_profile or reveal_profile.
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?
It provides clear usage context: 'Best for work emails' and warns that 'free-mailbox addresses often have no public profile to match,' which serves as a when-to-use and when-not-to-use signal. It does not name sibling alternatives for routing, but the work-email versus free-mailbox guidance is actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.