Tool ID:
predictleads_discover_news_eventsRun this action
Use the TypeScript SDK for one call. Usectx.tools.execute(...) inside a Play for durable calls, scheduled calls, or CSV processing.
import { Deepline } from 'deepline';
const deepline = await Deepline.connect();
const result = await deepline.tools.execute(
'predictleads_discover_news_events',
{
"categories": [
"acquires"
],
"company_location": "example",
"page": 1
},
);
console.log(result.toolResponse.raw);
CLI
deepline tools execute predictleads_discover_news_events --input '{
"categories": [
"acquires"
],
"company_location": "example",
"page": 1
}' --json
Example response
The static output schema lacks the details for a reliable example. Usedeepline describe predictleads_discover_news_events --json for the complete live contract.
Input reference
Returns a list of News Events, optionally filtered by categories and company’s location, ordered by updated date.| Name | Type | Required | Default | Details |
|---|---|---|---|---|
payload.categories | array | No | — | Comma-separated (,) NewsEvent categories. |
payload.company_location | string | No | — | The response will include only companies located in the given country name or state name/abbreviation. |
payload.page | integer | No | 1 | Page number of shown items. NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons. Minimum: 1. |
payload.limit | integer | No | 30 | Limit the number of shown items per page. Maximum: 1000. |
This input schema is too large for an inline display. Read the complete live contract with
deepline describe predictleads_discover_news_events --json.Output reference
Standard tool result payload.| Name | Type | Required | Default | Details |
|---|---|---|---|---|
result.data | object | Yes | — | Since 2016, PredictLeads has detected over 8 million relevant news signals, which are available for 2 million companies globally. The News Events are sourced from some 19 million blogs, news and PR sites and distilled into relevant categories by machine learning algorithms. The News Events dataset includes fields such as Formatted Signal, Signal Category, Most Relevant Source URL, Article Sentence, Article Body, Article Author, Found At, Effective Date and other News Event information. |
result.data.data | array | Yes | — | — |
result.data.data[].id | string | Yes | — | ID of the NewsEvent object. Format: uuid. |
result.data.data[].type | "news_event" | Yes | — | Type of the NewsEvent object. |
result.data.data[].attributes | object | Yes | — | Attributes of the NewsEvent object. |
result.data.data[].attributes.summary | string | Yes | — | A short, human readable excerpt of this NewsEvent data. |
result.data.data[].attributes.category | "acquires" | "merges_with" | "sells_assets_to" | "loses_client" | "signs_new_client" | "declares_bankruptcy" | "files_suit_against" | "has_issues_with" | "closes_offices_in" | "decreases_headcount_by" | "attends_event" | "expands_facilities" | "expands_offices_in" | "expands_offices_to" | "increases_headcount_by" | "opens_new_location" | "goes_public" | "has_earnings" | "has_revenue" | "has_valuation" | "invests_into" | "invests_into_assets" | "receives_financing" | "hires" | "leaves" | "promotes" | "retires_from" | "integrates_with" | "is_developing" | "launches" | "ends_partnership_with" | "partners_with" | "receives_award" | "recognized_as" | "identified_as_competitor_of" | "spins_off_company" | "spins_off_division" | Yes | — | Name of the category this NewsEvent represents. |
result.data.data[].attributes.found_at | string | Yes | — | Date & time (ISO 8601) when the NewsEvent was first discovered. As each NewsEvent aggregate the same signal from multiple sources (NewsArticles), this attribute represents the most common or earliest published_at date of these NewsArticles. Format: date-time. |
result.data.data[].attributes.confidence | number | Yes | — | A numerical score between 0 and 1 that represents PredictLeads’ estimated reliability of a news event. This value is always 1 when the event has been manually reviewed and approved by a human analyst. It can also be 1 for certain automatically detected events when the system determines they meet the highest certainty threshold. PredictLeads already balances match rate and accuracy by limiting NewsEvents, so we don’t recommend applying additional filters based on the confidence score. Minimum: 0. See the live schema for the complete constraint. |
result.data.data[].attributes.article_sentence | string | Yes | — | Sentence in the article where NewsEvent information was found. |
result.data.data[].attributes.planning | boolean | Yes | — | When true, the NewsEvent is planned to happen. |
result.data.data[].attributes.amount | string | null | Yes | — | This is a string of a value as extracted from text. |
result.data.data[].attributes.amount_normalized | integer | null | Yes | — | Integer value of “amount” property in USD. |
result.data.data[].attributes.assets | string | null | Yes | — | Assets found in the NewsEvent. |
result.data.data[].attributes.assets_tags | array | Yes | — | An array of assets tags extracted from “assets” property. |
result.data.data[].attributes.award | string | null | Yes | — | Award type this NewsEvent is announcing. |
result.data.data[].attributes.contact | string | null | Yes | — | Person name that the NewsEvent mentions. |
result.data.data[].attributes.event | string | null | Yes | — | The name of the event the company attended. |
result.data.data[].attributes.effective_date | string | null | Yes | — | Date (ISO 8601) the NewsEvent is mentioning. Format: date. |
result.data.data[].attributes.division | string | null | Yes | — | Whether NewsEvent concerns a specific division. |
result.data.data[].attributes.financing_type | string | null | Yes | — | A financing type this NewsEvent is mentioning. |
result.data.data[].attributes.financing_type_normalized | "pre_angel" | "angel_plus" | "angel_plus_plus" | "angel" | "angel_1" | "angel_2" | "angel_3" | "pre_seed" | "seed_plus" | "seed_plus_plus" | "seed" | "seed_1" | "seed_2" | "seed_3" | "pre_series_a" | "series_a_plus" | "series_a_plus_plus" | "series_a" | "series_a1" | "series_a2" | "series_a3" | "pre_series_b" | "series_b_plus" | "series_b_plus_plus" | "series_b" | "series_b1" | "series_b2" | "series_b3" | "pre_series_c" | "series_c_plus" | "series_c_plus_plus" | "series_c" | "series_c1" | "series_c2" | "series_c3" | "pre_series_d" | "series_d_plus" | "series_d_plus_plus" | "series_d" | "series_d1" | "series_d2" | "series_d3" | "pre_series_e" | "series_e_plus" | "series_e_plus_plus" | "series_e" | "series_e1" | "series_e2" | "series_e3" | "pre_series_f" | "series_f_plus" | "series_f_plus_plus" | "series_f" | "series_f1" | "series_f2" | "series_f3" | "pre_series_g" | "series_g_plus" | "series_g_plus_plus" | "series_g" | "series_g1" | "series_g2" | "series_g3" | "pre_series_h" | "series_h_plus" | "series_h_plus_plus" | "series_h" | "series_h1" | "series_h2" | "series_h3" | "pre_series_i" | "series_i_plus" | "series_i_plus_plus" | "series_i" | "series_i1" | "series_i2" | "series_i3" | "pre_series_j" | "series_j_plus" | "series_j_plus_plus" | "series_j" | "series_j1" | "series_j2" | "series_j3" | "pre_angel_bridge" | "angel_plus_bridge" | "angel_plus_plus_bridge" | "angel_bridge" | "angel_1_bridge" | "angel_2_bridge" | "angel_3_bridge" | "pre_seed_bridge" | "seed_plus_bridge" | "seed_plus_plus_bridge" | "seed_bridge" | "seed_1_bridge" | "seed_2_bridge" | "seed_3_bridge" | "pre_series_a_bridge" | "series_a_plus_bridge" | "series_a_plus_plus_bridge" | "series_a_bridge" | "series_a1_bridge" | "series_a2_bridge" | "series_a3_bridge" | "pre_series_b_bridge" | "series_b_plus_bridge" | "series_b_plus_plus_bridge" | "series_b_bridge" | "series_b1_bridge" | "series_b2_bridge" | "series_b3_bridge" | "pre_series_c_bridge" | "series_c_plus_bridge" | "series_c_plus_plus_bridge" | "series_c_bridge" | "series_c1_bridge" | "series_c2_bridge" | "series_c3_bridge" | "pre_series_d_bridge" | "series_d_plus_bridge" | "series_d_plus_plus_bridge" | "series_d_bridge" | "series_d1_bridge" | "series_d2_bridge" | "series_d3_bridge" | "pre_series_e_bridge" | "series_e_plus_bridge" | "series_e_plus_plus_bridge" | "series_e_bridge" | "series_e1_bridge" | "series_e2_bridge" | "series_e3_bridge" | null | Yes | — | A normalized value of financing_type property, where possible. Allowed: pre_angel, angel_plus, angel_plus_plus, angel, angel_1, angel_2, angel_3, pre_seed, seed_plus, seed_plus_plus, seed, seed_1, seed_2, seed_3, pre_series_a, series_a_plus, series_a_plus_plus, series_a, series_a1, series_a2, series_a3, pre_series_b, series_b_plus, series_b_plus_plus, series_b, series_b1, series_b2, series_b3, pre_series_c, series_c_plus, See the live schema for the complete constraint. |
result.data.data[].attributes.financing_type_tags | array | Yes | — | An array of financing type categories. |
result.data.data[].attributes.headcount | integer | null | Yes | — | A number of people mentioned organization is involved with. |
result.data.data[].attributes.job_title | string | null | Yes | — | Job title this NewsEvent is mentioning. |
result.data.data[].attributes.job_title_tags | array | Yes | — | An array of job title tags. |
result.data.data[].attributes.location | string | null | Yes | — | Location of where the NewsEvent happened or where the organization has expanded/relocated to. NOTE: It is built from the location_data attributes if present. Otherwise, it falls back to the original unnormalized detected location string. |
result.data.data[].attributes.location_data | array | Yes | — | NOTE: Currently contains at most one object. |
result.data.data[].attributes.location_data[].city | string | null | Yes | — | City of the location. |
result.data.data[].attributes.location_data[].state | string | null | Yes | — | State of the location. |
result.data.data[].attributes.location_data[].zip_code | string | null | Yes | — | ZIP code of the location. |
result.data.data[].attributes.location_data[].country | string | null | Yes | — | Country of the location. |
result.data.data[].attributes.location_data[].region | string | null | Yes | — | Region of the location. |
result.data.data[].attributes.location_data[].continent | string | null | Yes | — | Continent of the location. |
result.data.data[].attributes.location_data[].fuzzy_match | boolean | null | Yes | — | true indicates that location data may not have been extracted accurately. false indicates that location data has been extracted correctly. |
result.data.data[].attributes.product | string | null | Yes | — | Name of the product as mentioned by this NewsEvent. |
result.data.data[].attributes.product_data | object | Yes | — | — |
result.data.data[].attributes.product_data.full_text | string | null | Yes | — | Full product name as recognized by Named Entity Recognition, without further cleaning of the product name. |
result.data.data[].attributes.product_data.name | string | null | Yes | — | Cleaned name of the product referenced in the NewsEvent. |
result.data.data[].attributes.product_data.release_type | string | null | Yes | — | Product release type. |
result.data.data[].attributes.product_data.release_version | string | null | Yes | — | Product release version. |
result.data.data[].attributes.product_data.fuzzy_match | boolean | null | Yes | — | When true we might not have extracted the product name cleanly. When false exact product name should be extracted well. |
result.data.data[].attributes.product_tags | array | Yes | — | List of tags regarding the product. |
result.data.data[].attributes.recognition | string | null | Yes | — | Name of the recognition the company received. |
result.data.data[].attributes.vulnerability | string | null | Yes | — | Company issue. |
result.data.data[].relationships | object | Yes | — | The data contains references to related objects listed in the included property. |
result.data.data[].relationships.company1 | object | No | — | The data contains a reference to related CompanyLite object listed in the included property. |
result.data.data[].relationships.company1.data | object | Yes | — | — |
result.data.data[].relationships.company1.data.id | string | Yes | — | ID of the included CompanyLite object. Format: uuid. |
result.data.data[].relationships.company1.data.type | "company" | Yes | — | Type of the included CompanyLite object. |
This output schema is too large for an inline display. Read the complete live contract with
deepline describe predictleads_discover_news_events --json.Deepline cost
- Pricing model:
per_result(per result). - Estimated Deepline credits:
0.56per pricing unit.