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scalably-io

Hunter MCP

by scalably-io

email_finder

Read-only

Find and automatically verify a professional email address using a company domain and person's name, or from a LinkedIn handle. Turns name and domain inputs into a reachable email.

Instructions

Find and automatically verify the most likely professional email from a company/domain plus name, or directly from a LinkedIn handle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNo
companyNo
full_nameNo
last_nameNo
first_nameNo
max_durationNo
linkedin_handleNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint and openWorldHint annotations, the description discloses that the tool not only finds an email but also automatically verifies it, and that the result is a 'most likely' match. This adds meaningful behavioral context about heuristic searching and verification that annotations alone do not convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The entire description is one compact, front-loaded sentence with no filler. It states the action, the resource, and the primary input modes in an efficient way.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 7 optional-looking parameters, no required fields, no output schema, and no schema descriptions, the description leaves critical gaps: valid parameter combinations, required inputs, return format, and verification failure behavior. The tool's behavior is not fully specified for an agent to call it confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description only loosely maps the domain/company/name parameters and the LinkedIn handle. It does not clarify the relationship between domain and company, whether full_name vs first_name/last_name matters, or what max_duration controls. The description only partially compensates for the missing schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific action ('Find and automatically verify') and a specific resource ('professional email'), and names the two main input paths. It inherently distinguishes itself from email_verifier, since it finds rather than merely verifies, though it does not explicitly name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It communicates the intended input combinations: company/domain plus name, or LinkedIn handle. However, it provides no guidance on when to prefer this tool over siblings like email_enrichment, domain_search, or email_verifier, and no exclusions or preconditions are stated.

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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