Flowsery

Track Payment

track_payment

Record a payment for revenue attribution. If you use Stripe, LemonSqueezy, or Polar, payments are tracked automatically — only use this for other providers.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	No	Customer name
`email`	No	Customer email
`amount`	Yes	Payment amount (e.g. 29.99)
`domain`	No	Website domain to query. Required when using a workspace token unless websiteId is provided.
`currency`	Yes	Currency code (e.g. "USD", "EUR")
`isRefund`	No	True for refunded payments
`isRenewal`	No	True for recurring/renewal payments
`websiteId`	No	Website ID to query. Required when using a workspace token unless domain is provided.
`customerId`	No	Customer ID from payment provider
`sessionUid`	No	Session ID for the current visitor session
`visitorUid`	No	Visitor UID from _fs_vid cookie — strongly recommended for accurate revenue attribution
`transactionId`	Yes	Unique transaction ID from your payment provider

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	No	Confirmation of the recorded payment.

TDQS

A4/5.0

Behavior3/5

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

Annotations already indicate this is not read-only, so the write behavior is expected. The description adds useful context that it affects revenue attribution and warns against duplicate manual tracking for supported providers. However, it does not mention idempotency, duplicate-transaction handling, or other side effects.

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

Conciseness5/5

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

Two short, front-loaded sentences. The first states the core purpose, and the second adds the critical exclusion. Every sentence earns its place with no filler.

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

Completeness4/5

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

For a 12-parameter write tool, the description plus fully documented schema is sufficient for an agent to select and invoke it. It could have added a sentence about duplicate prevention or the meaning of the response, but the output schema exists and the parameter documentation is comprehensive.

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

Parameters3/5

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

The input schema has 100% description coverage for all 12 parameters, so the baseline is 3. The description does not add field-level semantics beyond saying payments are recorded for revenue attribution.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource ('Record a payment') and adds the business purpose 'for revenue attribution.' It is likely distinguishable from siblings like delete_payments and track_goal by name, but it does not explicitly name or contrast a sibling tool.

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 explicit when-to-use and when-not-to-use guidance: Stripe, LemonSqueezy, and Polar are automatically tracked, so this tool should only be used for other providers. This leaves no ambiguity about when the agent should call 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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TDQS

A3.6/5.0

Disambiguation5/5

Each tool serves a distinct purpose: get_* tools cover different analytics dimensions (browsers, cities, countries, etc.) with clear scopes, while issue management and tracking tools are clearly separated. Even get_breakdown is explicitly scoped to dimensions without dedicated endpoints, avoiding overlap.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case (delete_goals, get_overview, track_goal, update_issue_status). Verbs are specific and predictable (get, delete, track, list, update), and there are no naming style mixes.

Tool Count2/5

At 27 tools, the set exceeds the 25-tool threshold considered 'too many.' While the domain is broad (analytics, tracking, issues), many get_* tools could be consolidated into get_breakdown with filter parameters, making the count feel inflated rather than necessarily well-scoped.

Completeness4/5

The tool surface covers the full analytics lifecycle: overview, time series, breakdowns, real-time data, visitor profiles, goal/payment tracking and deletion, plus AI issue listing/detail/status updates. Minor gaps exist (e.g., no update for goals/payments, no explicit list of defined goals), but these are non-critical and don't impair typical workflows.

Resources

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