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revenue.sum

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Calculate total revenue from events, optionally grouped by traffic source or campaign. Supports first-touch and last-touch attribution for understanding customer acquisition.

Instructions

Sum revenue from Money-typed event properties. Returns per-currency totals, optionally grouped by a traffic dimension (referrer_host, channel, country, device_type, pathname, utm_source/medium/campaign). Different currencies are never mixed in a single sum — each row is one (group, currency) pair. Money properties are tracked via clamp.track("purchase", { total: { amount: 29, currency: "USD" } }) — see /docs/concepts/revenue for the full Money type.

Attribution: attribution_model="last_touch" (default) groups revenue by the dimensions of the session where the revenue event fired — answers "what surface was active at conversion?". attribution_model="first_touch" joins each revenue event with the visitor's earliest-known session and groups by that session's acquisition dimension — answers "where did my paying customers actually come from?". For multi-visit funnels (typical B2B SaaS), first-touch is usually the more honest read on which channels actually drive revenue.

Examples:

  • "total revenue this month" → no group_by, period="30d"

  • "revenue by channel" → group_by="channel", period="30d"

  • "where did paying customers actually come from" → group_by="channel", attribution_model="first_touch"

  • "first-touch revenue per UTM campaign" → group_by="utm_campaign", attribution_model="first_touch"

  • "how much did Stripe purchases bring in from organic search" → event="purchase", channel="organic_search"

  • "top revenue countries" → group_by="country"

Limitations: events without any Money property contribute zero. If property is set, only that one Money key is summed; omitted, every Money property on matched events is included. Stripe-typed revenue (recommended) flows through server-side webhooks; client-only revenue is subject to ad-blocker loss. attribution_model="first_touch" only supports acquisition-dimension group_by (channel, referrer_host, utm_*); first-touch country / device / pathname are rejected because they're rarely what people actually mean by them. First-touch breakdowns are extremely sensitive to small samples — at single-digit paying-customer counts the per-channel rate is dominated by 1-2 users' acquisition history; check visitors before reading the rate, or pair with users.journey to validate against specific customers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoTarget project ID (e.g. "proj_abc123"). Required when the credential has access to multiple projects. If omitted and only one project is accessible, that project is used automatically. Call `projects.list` to discover available project IDs.
periodNoTime period. Use "today", "yesterday", "7d", "30d", "90d", or a custom range as "YYYY-MM-DD:YYYY-MM-DD" (e.g. "2026-01-01:2026-03-31"). Defaults to "30d".
eventNoFilter to a specific event name (e.g. "purchase", "checkout_completed"). Omit to sum Money properties across all events.
propertyNoRestrict the sum to a single Money property key on the event (e.g. "total", "mrr", "ltv"). Omit to sum every Money-typed property on matched events.
group_byNoGroup revenue by a traffic dimension. Returns one row per (dimension, currency) pair. Omit for a single total per currency.
attribution_modelNoAttribution model. "last_touch" (default) groups by the dimensions of the session where revenue fired. "first_touch" joins each revenue event with the visitor's earliest session and groups by that session's acquisition dim. First-touch only supports group_by in acquisition dimensions (channel, referrer_host, utm_*).
limitNoMax rows to return (1-50). Defaults to 10.
pathnameNoFilter to a specific page path (e.g. "/pricing", "/blog/my-post"). Must start with /.
utm_sourceNoFilter by UTM source (e.g. "google", "twitter", "newsletter"). Case-sensitive, must match the value in the tracking URL.
utm_mediumNoFilter by UTM medium (e.g. "cpc", "email", "social"). Case-sensitive.
utm_campaignNoFilter by UTM campaign name (e.g. "spring-launch", "product-hunt"). Case-sensitive.
utm_contentNoFilter by UTM content (e.g. "hero-cta", "sidebar-banner"). Case-sensitive.
utm_termNoFilter by UTM term (e.g. "running+shoes"). Case-sensitive.
referrer_hostNoFilter by referrer hostname (e.g. "news.ycombinator.com", "twitter.com", "github.com"). Use this to see what traffic from a specific source did. Must match the value returned by `traffic.breakdown(dimension="referrer_host")` exactly (lowercase, no protocol or path).
countryNoISO 3166-1 alpha-2 country code, uppercase (e.g. "US", "GB", "DE", "NL", "JP"). Filter results to visitors from this country.
regionNoAdministrative region inside a country (e.g. "California", "Bavaria"). Case-sensitive; must match the stored region exactly. Use traffic.breakdown(dimension="region") to discover values.
cityNoCity name (e.g. "San Francisco", "London"). Case-sensitive; must match the stored value. Use traffic.breakdown(dimension="city") to discover values.
device_typeNoDevice category. One of: "desktop", "mobile", "tablet".
browserNoBrowser family (e.g. "Chrome", "Safari", "Firefox"). Use traffic.breakdown(dimension="browser") to discover the exact stored values.
browser_versionNoBrowser version string (e.g. "120.0"). Case-sensitive.
osNoOperating system family (e.g. "macOS", "iOS", "Windows", "Android"). Use traffic.breakdown(dimension="os") to discover stored values.
os_versionNoOS version string (e.g. "14.2"). Case-sensitive.
channelNoTraffic channel. One of: "direct", "organic_search", "organic_social", "paid", "email", "referral".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
Behavior5/5

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

Discloses key behaviors beyond readOnlyHint: never mixes currencies, attribution model differences, first-touch limitations, ad-blocker loss for client-only revenue. No contradiction with annotations.

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?

Description is long but well-organized into sections (core, attribution, examples, limitations). Front-loaded with purpose. A minor condensation could be beneficial, but every sentence 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?

Given 23 optional parameters, existence of output schema, and the complexity of revenue attribution, the description covers usage patterns, edge cases, and limitations comprehensively for an AI agent.

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

Parameters5/5

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

Schema coverage is 100% with clear descriptions. The description adds significant value: explains attribution models in detail, provides practical examples, clarifies default behaviors (no group_by gives total per currency, property omitted sums all Money properties).

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 the tool sums revenue from Money-typed event properties, returns per-currency totals, and optionally groups by a traffic dimension. It distinguishes from sibling tools (e.g., traffic.breakdown, funnels) by focusing on revenue summation.

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

Usage Guidelines4/5

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

Provides explicit when-to-use (sum revenue) and when-not (events without Money property contribute zero). Includes examples and limitations (first-touch only supports acquisition dimensions, small-sample sensitivity). Does not explicitly name alternative tools but the context signals and sibling list provide differentiation.

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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