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Company Install Time Series

Coming soon

The v2 MCP API is in preview and not yet generally available. These pages document its tools and request/response shapes; live data access is limited to enrolled organizations until v2 is released.

Track how a company's technology adoption changes over TIME: returns a monthly installation-intensity time series per product for one company. Use this for TREND questions — adoption growth, decline, or churn — e.g. 'How has Cisco's usage of Snowflake changed over the past 2 years?' or 'Is company X ramping up or winding down its AWS footprint?' Identify the company by EITHER company_domain OR hg_id — provide exactly one (supplying both or neither is a validation error). Do NOT use this for a point-in-time answer: for the company's CURRENT installed tech stack use company_technographic (snapshot); for department/role usage use company_fai; for dollar spend use company_spend. Each data_points[].intensity is an integer 1-31 = days the product was detected that month (null = no detection). The most-recent point is typically null (current month incomplete); treat a partial penultimate point as provisional. For trend analysis use intensity_momentum (positive = growing, negative = declining; magnitude is meaningful), not raw intensity; current_intensity is a separate aggregate and NOT on the 1-31 daily scale. Filtering is ID-based only — numeric product_ids/vendor_ids or string category_ids (no name-based filtering); resolve IDs first (see each param). Filter IDs that match nothing return products: [] with HTTP 200 and 0 credits — indistinguishable from genuine no-data, so this tool sets the warning field whenever filters were provided but nothing matched. Use country_codes with granularity='country' for per-country breakdowns. Credit cost: 3 per product returned; 0 on empty results.

Parameters​

NameTypeRequiredDefaultDescription
company_domainstring❌ No-The company domain to look up (e.g., 'cisco.com'). Provide EITHER company_domain OR hg_id — exactly one is required. Protocol prefixes (http://, https://), leading www., and trailing paths/queries/fragments are accepted and stripped automatically; case is normalized. match_confidence in the response is 0.90 when identified by domain.
hg_idstring❌ No-The hex HG company id (31-32 alphanumeric chars) as returned by search_companies. Provide EITHER hg_id OR company_domain — exactly one is required. match_confidence in the response is 1.0 when identified by hg_id.
product_idsarray❌ No-Filter by numeric HG product IDs (integers, e.g. [26434, 22]). Resolve IDs with product_search_and_enrich first — the upstream filters by integer ID only, so names or slug-style IDs return nothing.
vendor_idsarray❌ No-Filter by numeric HG vendor IDs (integers, e.g. [376]). Resolve IDs with get_vendor_information first — the upstream filters by integer ID only.
category_idsarray❌ No-Filter by HG category IDs (strings, e.g. ['cat-crm']). Resolve IDs with list_product_categories first — the upstream filters by category ID only, not name.
country_codesarray❌ No-ISO 3166-1 alpha-2 country codes (e.g. ['US', 'GB']). Use with granularity='country' to get per-country intensity breakdowns; each returned product then carries a country_code.
granularitystring❌ No-'global' aggregates intensity across all countries (default upstream behavior). 'country' returns one row per product per country with country_code populated on each product.
time_rangestring❌ Nolast_24_monthsTime range for the series. Options: last_6_months, last_12_months, last_24_months, last_36_months. Default: last_24_months. Note: each option returns N+1 data points because the current incomplete month is appended as a null tail (e.g. last_6_months → 7 points, last_12_months → 13 points).
max_resultsinteger❌ No10Maximum number of products to return (1-50, default 10).

Required Integrations​

  • hginsights_v2__data_api

Use Cases​

  • How has a company's usage of a product trended over the past N months? — filter by product_ids
  • Is a company ramping up or winding down a specific vendor's footprint? — read intensity_momentum after a vendor_ids filter
  • Detect adoption growth or churn across a company's tech stack over time — unfiltered call, inspect data_points per product
  • Compare recent momentum across a category of tools at a company — category_ids filter, sort by intensity_momentum
  • Confirm whether a product's decline is recent or long-running — widen time_range to last_36_months

Example Usage​

Cisco's tech-adoption trend over the last 2 years (default range)

{
  "tool": "company_install_time_series",
  "arguments": {
    "company_domain": "cisco.com"
  }
}

Snowflake usage trend at Cisco over the last 12 months (by product ID)

{
  "tool": "company_install_time_series",
  "arguments": {
    "company_domain": "cisco.com",
    "product_ids": [
      26434
    ],
    "time_range": "last_12_months"
  }
}

Per-country momentum for a specific vendor over 36 months

{
  "tool": "company_install_time_series",
  "arguments": {
    "company_domain": "cisco.com",
    "vendor_ids": [
      376
    ],
    "country_codes": [
      "US",
      "GB"
    ],
    "granularity": "country",
    "time_range": "last_36_months"
  }
}

company_technographic, product_search_and_enrich, get_vendor_information, get_product_category, company_spend, company_fai