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Reveal contact emails

reveal_contacts

Spend one of the user's monthly contact reveals to find and verify decision-maker emails at a company, matched to the titles they sell to. Use this when get_company_contacts returns nobody, or nobody with an email. Charges the monthly allowance (Strategic 600, Pro 150); tells the user what is left and how to upgrade when it runs out.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYesCompany id or slug to reveal contacts at
include_mobileNoAlso attempt a mobile number. Costs considerably more than an email, so only when the user asks for a phone number.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing the cost model: it consumes one of the user's monthly reveals, quantifies the allowance per plan (Strategic 600, Pro 150), and states that it reports remaining quota and upgrade path. This makes the non-idempotent, consumptive nature concrete, which the bare annotations (readOnlyHint=false, idempotentHint=false) only hint at.

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?

Three tight sentences: purpose first, trigger condition second, cost/completion behavior third. Every sentence carries distinct information with no filler.

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

Completeness4/5

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

Covers the essentials for a metered spend action: trigger, cost, quota feedback, and upgrade path, which is meaningful given there is no output schema. It stops short of describing the returned shape or the no-result/error case, so it is strong but not exhaustive.

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

Parameters3/5

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

Schema coverage is 100%, so both parameters (company, include_mobile) are already documented, including the mobile cost caveat. The description adds behavioral context about matching to sold titles but no additional parameter-level syntax or format detail, so the baseline 3 applies.

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

Purpose5/5

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

States a specific action (spend a reveal to find and verify decision-maker emails) with a clear resource and scope ('matched to the titles they sell to'). It distinguishes itself from get_company_contacts by naming the sibling and the situation that selects it.

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

Usage Guidelines5/5

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

Explicitly gives the when-to-use trigger: 'Use this when get_company_contacts returns nobody, or nobody with an email.' It names the alternative tool and the exact condition that routes to this one, leaving nothing to inference.

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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