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Create G2 Intent - LinkedIn Native (Dynamic) Audience

create_g2_intent_linkedin_native_dynamic_audience

Create a G2 Intent - LinkedIn Native (Dynamic) audience (platform customAudienceType=DYNAMIC_G2).

            AUDIENCE TYPE (mirrors the UI's "Audience Type" dropdown):
              • UI label: "G2 Intent - LinkedIn Native (Dynamic)"
              • Platform enum: DYNAMIC_G2
              • Refreshes daily as G2 intent signals update; targets LinkedIn natively.

            PREREQUISITE:
              • Both G2 and LinkedIn integrations MUST be connected. If either is missing, do NOT call this tool — recommend `create_firmographic_audience` instead.

            WHEN TO USE (exact user phrasing this tool should match):
              • "G2 Intent - LinkedIn Native (Dynamic)"
              • "G2 LinkedIn Native Dynamic"
              • "LinkedIn native G2 intent audience"
              • The user explicitly mentions BOTH G2 intent AND LinkedIn native targeting.

            WHEN NOT TO USE:
              • If the user asked for "G2 Intent (Dynamic)" without "LinkedIn Native" → use `create_g2_intent_dynamic_audience`.
              • If the user asked for "G2 Intent (Static)" → use `create_g2_intent_static_audience`.

            BUYING STAGES — REQUIRED BY THE PLATFORM:
              The platform UI marks Buying Stages as required. If the user did not name any stages, STOP and ask the user which of AWARENESS / CONSIDERATION / DECISION to target. DO NOT silently default — that produced wrong audiences in PRD-29702 / PRD-29703.

            CRITERIA (LinkedIn-native shapes; free-text fields are resolved server-side via the LinkedIn references API):
              • employees — LinkedIn-native employee ranges. Valid labels: see the schema (e.g. "201-500", "501-1000", "1001-5000").
              • revenues — LinkedIn-native revenue ranges (e.g. "$1M-$10M", "$10M-$100M").
              • company_names — free-text company names (resolved to LinkedIn company IDs).
              • location_country_ids — country IDs (e.g. 229=US, 228=UK).
              • job_titles — free-text titles (resolved to LinkedIn job-title IDs).
              • skills — free-text professional skills (resolved to LinkedIn skill IDs).

            PARAMETERS:
              • name (required, ≤ 50 chars)
              • intent_days (required, 1-365)
              • buying_stages (REQUIRED by platform — ask the user if missing; do NOT default)
              • employees, revenues, company_names, location_country_ids, job_titles, skills (all optional)

            RETURNS: id, audience_id, audience_name, audience_type (DYNAMIC_G2), status, buying_stages, intent_days, expectedNumberOfCompanies, expectedNumberOfContacts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesAudience name (required, max 50 characters).
skillsNoFree-text professional skills (resolved to LinkedIn skill IDs). Example: ['HubSpot', 'Salesforce', 'Python']
revenuesNoLinkedIn revenue ranges for audience targeting. Valid values: Under $1M, $1M-$10M, $10M-$100M, $100M-$1B, $1B+. Example: ['$1M-$10M', '$10M-$100M']
employeesNoLinkedIn employee count ranges for audience targeting. Valid values: 1, 2-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, +10001. Example: ['201-500', '501-1000']
job_titlesNoFree-text job titles (resolved to LinkedIn job-title IDs). Example: ['Software Engineer', 'Product Manager']
intent_daysYesDays to look back for G2 intent signals (required, 1-365).
buying_stagesNoBuying stages to target. The platform REQUIRES at least one; if the user did not specify, ASK them before calling — do NOT default.
company_namesNoFree-text company names to target (resolved server-side to LinkedIn company IDs). Example: ['Metadata', 'Google', 'Salesforce']
location_country_idsNoCountry IDs for geographic targeting (optional). Valid IDs: 1 (Afghanistan), 2 (Albania), 3 (Algeria), 4 (American Samoa), 5 (Andorra), 6 (Angola), 7 (Anguilla), 8 (Antarctica), 9 (Antigua and Barbuda), 10 (Argentina), 11 (Armenia), 12 (Aruba), 13 (Australia), 14 (Austria), 15 (Azerbaijan), 16 (Bahamas), 17 (Bahrain), 18 (Bangladesh), 19 (Barbados), 20 (Belarus), 21 (Belgium), 22 (Belize), 23 (Benin), 24 (Bermuda), 25 (Bhutan), 26 (Bolivia), 27 (Bosnia and Herzegovina), 28 (Botswana), 29 (Brazil), 30 (British Indian Ocean Territory), 31 (British Virgin Islands), 32 (Brunei), 33 (Bulgaria), 34 (Burkina Faso), 35 (Burundi), 36 (Cambodia), 37 (Cameroon), 38 (Canada), 39 (Cape Verde), 40 (Cayman Islands), 41 (Central African Republic), 42 (Chad), 43 (Chile), 44 (China), 45 (Christmas Island), 46 (Cocos Islands), 47 (Colombia), 48 (Comoros), 49 (Cook Islands), 50 (Costa Rica), 51 (Croatia), 53 (Curacao), 54 (Cyprus), 55 (Czech Republic), 56 (Democratic Republic of the Congo), 57 (Denmark), 58 (Djibouti), 59 (Dominica), 60 (Dominican Republic), 61 (East Timor), 62 (Ecuador), 63 (Egypt), 64 (El Salvador), 65 (Equatorial Guinea), 66 (Eritrea), 67 (Estonia), 68 (Ethiopia), 69 (Falkland Islands), 70 (Faroe Islands), 71 (Fiji), 72 (Finland), 73 (France), 74 (French Polynesia), 75 (Gabon), 76 (Gambia), 77 (Georgia), 78 (Germany), 79 (Ghana), 80 (Gibraltar), 81 (Greece), 82 (Greenland), 83 (Grenada), 84 (Guam), 85 (Guatemala), 86 (Guernsey), 87 (Guinea), 88 (Guinea-Bissau), 89 (Guyana), 90 (Haiti), 91 (Honduras), 92 (Hong Kong), 93 (Hungary), 94 (Iceland), 95 (India), 96 (Indonesia), 98 (Iraq), 99 (Ireland), 100 (Isle of Man), 101 (Israel), 102 (Italy), 103 (Ivory Coast), 104 (Jamaica), 105 (Japan), 106 (Jersey), 107 (Jordan), 108 (Kazakhstan), 109 (Kenya), 110 (Kiribati), 111 (Kosovo), 112 (Kuwait), 113 (Kyrgyzstan), 114 (Laos), 115 (Latvia), 116 (Lebanon), 117 (Lesotho), 118 (Liberia), 119 (Libya), 120 (Liechtenstein), 121 (Lithuania), 122 (Luxembourg), 123 (Macau), 124 (Macedonia), 125 (Madagascar), 126 (Malawi), 127 (Malaysia), 128 (Maldives), 129 (Mali), 130 (Malta), 131 (Marshall Islands), 132 (Mauritania), 133 (Mauritius), 134 (Mayotte), 135 (Mexico), 136 (Micronesia), 137 (Moldova), 138 (Monaco), 139 (Mongolia), 140 (Montenegro), 141 (Montserrat), 142 (Morocco), 143 (Mozambique), 144 (Myanmar), 145 (Namibia), 146 (Nauru), 147 (Nepal), 148 (Netherlands), 149 (Netherlands Antilles), 150 (New Caledonia), 151 (New Zealand), 152 (Nicaragua), 153 (Niger), 154 (Nigeria), 155 (Niue), 157 (Northern Mariana Islands), 158 (Norway), 159 (Oman), 160 (Pakistan), 161 (Palau), 162 (Palestine), 163 (Panama), 164 (Papua New Guinea), 165 (Paraguay), 166 (Peru), 167 (Philippines), 168 (Pitcairn), 169 (Poland), 170 (Portugal), 171 (Puerto Rico), 172 (Qatar), 173 (Republic of the Congo), 174 (Reunion), 175 (Romania), 176 (Russia), 177 (Rwanda), 178 (Saint Barthelemy), 179 (Saint Helena), 180 (Saint Kitts and Nevis), 181 (Saint Lucia), 182 (Saint Martin), 183 (Saint Pierre and Miquelon), 184 (Saint Vincent and the Grenadines), 185 (Samoa), 186 (San Marino), 187 (Sao Tome and Principe), 188 (Saudi Arabia), 189 (Senegal), 190 (Serbia), 191 (Seychelles), 192 (Sierra Leone), 193 (Singapore), 194 (Sint Maarten), 195 (Slovakia), 196 (Slovenia), 197 (Solomon Islands), 198 (Somalia), 199 (South Africa), 200 (South Korea), 201 (South Sudan), 202 (Spain), 203 (Sri Lanka), 205 (Suriname), 206 (Svalbard and Jan Mayen), 207 (Swaziland), 208 (Sweden), 209 (Switzerland), 211 (Taiwan), 212 (Tajikistan), 213 (Tanzania), 214 (Thailand), 215 (Togo), 216 (Tokelau), 217 (Tonga), 218 (Trinidad and Tobago), 219 (Tunisia), 220 (Turkey), 221 (Turkmenistan), 222 (Turks and Caicos Islands), 223 (Tuvalu), 224 (U.S. Virgin Islands), 225 (Uganda), 226 (Ukraine), 227 (United Arab Emirates), 228 (United Kingdom), 229 (United States), 230 (Uruguay), 231 (Uzbekistan), 232 (Vanuatu), 233 (Vatican), 234 (Venezuela), 235 (Vietnam), 236 (Wallis and Futuna), 237 (Western Sahara), 238 (Yemen), 239 (Zambia), 240 (Zimbabwe), 241 (Guadeloupe). Example: [29] for Brazil, [229] for United States

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only indicate readOnly=false, openWorld=true, destructive=false, so the description carries the full burden of behavioral disclosure. It discloses the platform-required buying-stages constraint with a do-NOT-default instruction and PRD references, explains server-side resolution of free-text criteria via the LinkedIn references API, and notes daily refresh behavior. This goes well beyond the structured metadata.

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

Conciseness4/5

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

The description is long but well-structured with clear sections (AUDIENCE TYPE, PREREQUISITE, WHEN TO USE, WHEN NOT TO USE, BUYING STAGES, CRITERIA, PARAMETERS, RETURNS). There is some redundancy—the parameter list largely repeats the schema, and the title is restated—but for a 9-parameter tool with sibling ambiguity, every major section earns its place.

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

Completeness5/5

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

With no output schema and a complex 9-parameter tool, the description compensates fully: it lists the return fields, documents the user-phrasing triggers and exclusions, calls out the platform-required buying-stages trap with explicit 'ask the user' guidance, and explains how criteria fields are resolved. Nothing an agent needs to call and interpret the result is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: it flags `buying_stages` as REQUIRED by the platform even though the schema marks it optional, instructs asking the user rather than defaulting, and clarifies that free-text fields like `company_names` and `job_titles` are resolved server-side via the LinkedIn references API. These additions help an agent invoke parameters correctly, though not every parameter receives extra semantic value.

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?

The description opens with a specific verb-resource pair: 'Create a **G2 Intent - LinkedIn Native (Dynamic)** audience' and names the platform enum `customAudienceType=DYNAMIC_G2`. It also differentiates from sibling tools through explicit 'WHEN NOT TO USE' guidance, so an agent can reliably select it over `create_g2_intent_dynamic_audience` and `create_g2_intent_static_audience`.

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?

Provides an exhaustive usage contract: a prerequisite ('Both G2 and LinkedIn integrations MUST be connected'), exact user phrasing to match, and explicit exclusion rules naming the alternative tools. It even instructs the agent to avoid calling the tool and route to `create_firmographic_audience` if prerequisites are unmet, leaving no ambiguity about when to use it.

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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