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Create CSV Upload - LinkedIn Native Audience

create_csv_upload_linkedin_native_audience

Create a CSV Upload - LinkedIn Native audience (platform customAudienceType=NATIVE_TARGETING_CSV).

            AUDIENCE TYPE (mirrors the UI's "Audience Type" dropdown):
              • UI label: "CSV Upload - LinkedIn Native"
              • Platform enum: NATIVE_TARGETING_CSV
              • Channel: LinkedIn only.
              • Two-step flow handled server-side: the CSV is uploaded as a NATIVE ABM list, then the audience is created from it with the LinkedIn-native firmographics + contact criteria applied.

            PREREQUISITE:
              • LinkedIn integration MUST be connected.

            WHEN TO USE (exact user phrasing this tool should match):
              • "CSV Upload - LinkedIn Native"
              • "Upload a CSV and target LinkedIn natively"
              • "LinkedIn native audience from this CSV"
              • The user attached a CSV of companies AND asked for LinkedIn-native targeting (employees / revenues / job titles / skills resolved via LinkedIn).

            WHEN NOT TO USE:
              • If the user asked for a plain "CSV Upload - Accounts" → use `upload_account_list_csv_audience` (creates FIRMOGRAPHIC_INCLUDE, NOT LinkedIn-native).
              • If the user asked for "Native Criteria - LinkedIn" without a CSV → use `create_linkedin_native_criteria_audience`.

            TWO WAYS TO SUPPLY THE ACCOUNT LIST — provide EXACTLY ONE of:
              • `companies`: inline `{<companyname>: <companywebsite>}` map (short ad-hoc lists).
              • `companies_source_csv_url`: URL of a CSV with header `companyname,companywebsite` (case-insensitive). The MCP server downloads, validates, and uploads it as a native ABM list.

            CRITERIA (all optional, LinkedIn-native shapes resolved server-side):
              • employees — LinkedIn employee ranges (e.g. "201-500", "501-1000").
              • revenues — LinkedIn revenue ranges (e.g. "$1M-$10M").
              • company_names — free-text company names (resolved to LinkedIn IDs).
              • location_country_ids — country IDs (e.g. 229=US).
              • job_titles — free-text titles (resolved to LinkedIn IDs).
              • skills — free-text skills (resolved to LinkedIn IDs).

            RETURNS: id, audience_id, audience_name, audience_type (NATIVE_TARGETING_CSV), status, abmSearchCriteriaId, companies_count, upload_filename, counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skillsNoFree-text professional skills (resolved to LinkedIn skill IDs). Example: ['HubSpot', 'Salesforce']
revenuesNoLinkedIn revenue ranges for audience targeting. Valid values: Under $1M, $1M-$10M, $10M-$100M, $100M-$1B, $1B+. Example: ['$1M-$10M', '$10M-$100M']
companiesNoInline map of company names to website URLs (optional). MUTUALLY EXCLUSIVE with `companies_source_csv_url`. Example: {"Acme Corp": "https://acme.com"}
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']
audience_nameYesName for the new audience (required).
company_namesNoFree-text company names (resolved to LinkedIn company IDs). Example: ['Metadata', 'Google']
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
companies_source_csv_urlNoPublic URL of a CSV with header EXACTLY `companyname,companywebsite` (case-insensitive). Use when the user attached a CSV to the chat — the URL comes via `AudienceBrief.attached_file_urls`. MUTUALLY EXCLUSIVE with `companies`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=false, openWorldHint=true, and destructiveHint=false, which the description does not contradict. The description adds context beyond annotations by explaining the server-side two-step flow (CSV upload as NATIVE ABM list, then audience creation) and the integration prerequisite. It does not describe side effects like reversibility or data retention, but that is minor given the write operation is clearly stated.

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 headings and bullet points, making it scannable. Every section contributes necessary information: audience type, prerequisites, usage boundaries, list options, criteria, and return values. It is verbose due to complexity but not wasteful.

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?

Given 9 parameters with 100% schema coverage, 1 required, and no output schema, the description covers all critical aspects: how to provide the account list, the criteria options, the server-side flow, prerequisites, and return fields. No missing information that an agent would need to invoke the tool correctly.

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 baseline is 3. The description adds value by explicitly stating the two ways to supply the account list (companies vs companies_source_csv_url) and that exactly one must be provided, plus clarifying criteria are optional and resolved server-side. This goes beyond the schema descriptions, warranting a 4.

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 clearly states it creates a CSV Upload - LinkedIn Native audience, specifies the exact platform enum (NATIVE_TARGETING_CSV), and differentiates from siblings like upload_account_list_csv_audience and create_linkedin_native_criteria_audience. The verb 'Create' and resource are explicit and unambiguous.

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?

The description includes a dedicated 'WHEN TO USE' section with exact user phrasing examples and a 'WHEN NOT TO USE' section that names alternative tools and conditions. This provides explicit routing guidance that an agent can act on without 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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