Why finance apps lead the ChatGPT app store in complexity
Three of the ten most tool-heavy apps in the ChatGPT App Store are finance apps. Gusto leads the entire store with 40 tools. Stripe has 26. S&P Global has 23. Finance as a category makes up just 12.2% of the store's 147 third-party apps, but it punches well above its weight on every complexity metric we measured.
We wanted to understand why, and what the data says about how financial services companies approach building for this platform compared to everyone else.
Finance in Context: 18 Apps, Outsized Complexity
The ChatGPT App Store's 147 third-party apps span 13 categories. Finance has 18 apps, putting it on par with Productivity (also 18). But look at the top of the tool-count leaderboard and the category distribution is lopsided:
| Rank | App | Tools | Category |
|---|---|---|---|
| 1 | Gusto | 40 | Finance |
| 2 | Monday.com | 35 | Productivity |
| 3 | Canva | 34 | Design |
| 4 | Atlassian Rovo | 34 | Collaboration |
| 5 | Retell AI | 28 | Business |
| 6 | Stripe | 26 | Finance |
| 7 | Klaviyo | 26 | Productivity |
| 8 | Egnyte | 23 | Collaboration |
| 9 | Cloudinary | 23 | Developer Tools |
| 10 | S&P Global | 23 | Finance |
No other category places three apps in the top 10. Design, Productivity, Collaboration, Business, and Developer Tools each contribute one or two. Finance contributes three, from entirely different domains within the category: payroll (Gusto), payments (Stripe), and market data (S&P Global).
Compare this to Shopping, which has 14 apps. Zen Shopping has a single tool: search_products_for_shopping. Most Travel apps cluster between 1 and 5 tools (Priceline has 1, Busbud has 1, Booking.com has a handful). These categories tend to optimize for a narrow, focused interaction: find a product, search a hotel, book a ride.
Finance apps, as a group, can't get away with that.
It's Not Just Tool Count: Parameters Tell a Deeper Story
Tool count alone doesn't capture the full picture. A tool with 2 parameters is fundamentally simpler than one with 15. When we looked at parameter counts across finance apps, the complexity gap widened further.
| App | Total Params | Required | Required % |
|---|---|---|---|
| Alpaca | 83 | 20 | 24.1% |
| Intuit TurboTax | 61 | 12 | 19.7% |
| S&P Global | 60 | 29 | 48.3% |
| Stripe | 57 | 21 | 36.8% |
| LSEG | 54 | 11 | 20.4% |
| Gusto | 51 | 27 | 52.9% |
| Ramp | 49 | 16 | 32.7% |
| Intuit QuickBooks | 33 | 7 | 21.2% |
| PitchBook | 24 | 12 | 50.0% |
| Morningstar | 13 | 8 | 61.5% |
| Tuio | 9 | 8 | 88.9% |
| PayPal | 7 | 2 | 28.6% |
| Kraken | 3 | 1 | 33.3% |
Six finance apps have 49 or more total parameters. Alpaca tops the list at 83 parameters across its tools. For context, the store-wide average sits much lower: 3,349 total parameters spread across 147 apps works out to roughly 23 parameters per app on average.
The required-field ratio adds another layer. Across the entire store, 34.58% of parameters are required. Several finance apps exceed this significantly. Gusto marks 52.94% of its parameters as required. S&P Global is at 48.33%. Morningstar reaches 61.54%. PitchBook sits at 50%. When a higher share of parameters is required, the LLM has less room to improvise or skip fields, and more work to do to construct a valid tool call.
Enum Constraints: Finance Apps Demand Precision
Enum parameters (parameters that accept only specific predefined values) are a signal of how tightly an app constrains inputs. Finance apps use them deliberately.
S&P Global has 7 enum-constrained parameters: capitalization, line_item, segment_type, statement, business_relationship, competitor_source, and periodicity. Each of these maps to a financial concept where free-text input would be dangerous. You don't want a model guessing whether "income_statement" should be "income statement" or "P&L" or "profit and loss" when querying a financial data API. The enum forces a controlled vocabulary.
Stripe has 5 enum parameters: language, reason, status, duration, and proration_behavior. These reflect Stripe's famously precise API design, where every input is validated against a known set of values. When you're updating a subscription's proration behavior, the only acceptable inputs might be create_prorations, none, or always_invoice, and that strictness exists for good reason.
LSEG uses 7 enum parameters including swapType, issuerType, and interval, constraining the kinds of financial instruments and time-series queries the model can construct.
Compare this to Shopping or Travel. Zen Shopping has just 1 enum param (sort_by). Booking.com has zero. The inputs for "find me a hotel in Paris" are fundamentally more forgiving than "get me the quarterly income statement for AAPL with as-reported figures in USD."
Why Finance Is Different: Three Structural Drivers
The data patterns we see aren't random. There are structural reasons finance apps end up more complex.
1. Regulatory and Compliance Requirements
Financial operations carry legal weight. Gusto's run_payroll and calculate_payroll are distinct tools because running payroll and calculating payroll are genuinely different operations with different compliance implications. You can't casually merge "show me what this payroll would look like" with "submit this payroll to the IRS." The tools need to be separate because the underlying workflows are required to be separate.
This shows up across the category. Intuit TurboTax's filing_status enum exists because the IRS defines exactly five filing statuses, and accepting free text would introduce errors with real financial consequences. S&P Global's statement enum distinguishes between balance_sheet, income_statement, and cashflow because these are standardized accounting concepts with precise definitions.
2. Workflow Granularity
Finance workflows involve many discrete steps that resist simplification. Look at Gusto's 40 tools: get_employee, get_compensation, get_payroll, list_pay_schedules, list_payroll_blockers, run_payroll, submit_payroll. Each of these represents a real step in a payroll manager's workflow. They can't be collapsed without losing critical functionality.
S&P Global has 23 tools because financial research is inherently multi-step. You might need to look up a company identifier (get_cusip_from_identifiers), pull its financials (get_financial_statement_from_identifiers), check its segments (get_segments_from_identifiers), and review earnings transcripts (get_transcript_from_key_dev_id). These are distinct research actions, not variations of the same query.
Stripe's 26 tools span the lifecycle of payment processing: creating customers, products, prices, invoices, payment links, subscriptions, refunds, and coupons. Each represents a distinct business operation. create_invoice and finalize_invoice are separate because in Stripe's model (and in accounting generally) drafting an invoice and finalizing it are different actions with different consequences.
3. Data Precision Requirements
Finance apps deal with data where ambiguity is costly. S&P Global's get_capitalization_from_identifiers accepts a capitalization enum because "market cap" can refer to different calculations depending on how you handle diluted shares, options, or convertible instruments. The enum removes that ambiguity.
This is fundamentally different from, say, a travel app where "near downtown" is an acceptable location input. Financial data apps need exact identifiers (CUSIPs, ISINs, ticker symbols), exact date ranges, exact statement types, and exact reporting periods. Every optional parameter that becomes required, and every string that becomes an enum, reflects a domain where imprecision has real costs.
The Range Within Finance: Not All Finance Apps Are Complex
It's worth noting that finance is not monolithic. The category spans a wide range:
- High complexity: Gusto (40 tools, 51 params), S&P Global (23 tools, 60 params), Alpaca (83 params)
- Moderate complexity: Stripe (26 tools, 57 params), Ramp (49 params), LSEG (54 params), Intuit TurboTax (61 params)
- Lower complexity: Morningstar (13 params), PayPal (7 params), Kraken (3 params), TickerSage (1 tool)
PayPal and Kraken are relatively lightweight. TickerSage has a single tool (live_kline). These are apps that focus on a narrow slice of financial functionality, and they look more like Shopping or Travel apps in their complexity profile. The apps that drive finance's outsized complexity numbers are the ones trying to expose full platform functionality: payroll management, payment infrastructure, financial data terminals.
The pattern suggests that complexity in finance isn't driven by the category label itself, but by the breadth of the underlying workflow. A crypto price chart (TickerSage) is simple. A payroll system (Gusto) is not.
What Other Categories Can Learn
Finance apps' approach to complexity offers some practical lessons for app builders in any category.
Use enums when precision matters. If your tool accepts inputs where specific values are important (status fields, sort orders, resource types), constrain them with enums rather than accepting free text. This helps the LLM make valid tool calls and reduces error rates. Even outside of finance, apps like Cloudinary (11 enum params) and Atlassian Rovo (21 enum params) use this pattern effectively.
Separate tools when operations have different consequences. The distinction between Gusto's calculate_payroll and run_payroll isn't pedantic; it reflects a real difference between a read operation and a write operation with legal implications. If your app has similar read/write or draft/finalize distinctions, separating them into distinct tools gives the LLM clearer choices and gives users more control.
High required-field ratios are a feature, not a bug. When Morningstar marks 61.54% of its parameters as required, it's ensuring that the model provides enough context for a valid query. If your tools are returning poor results because the model is calling them with minimal inputs, consider making more parameters required rather than optional.
Match your complexity to your workflow, not your category. TickerSage proves that a finance app can be a single-tool integration if the use case is narrow enough. PayPal stays lean at 7 parameters. Don't build 40 tools because you're in a "complex" category. Build what the workflow actually requires.
Open Questions
A few things we're still digging into:
- Does the higher complexity of finance apps affect how often ChatGPT successfully invokes their tools? More required parameters and more enums should theoretically improve accuracy, but the higher overall complexity could also increase failure rates.
- How do finance apps handle authentication? The store overall skews 56.46% NONE and 39.46% OAuth. Finance apps, given their sensitivity, likely lean more heavily toward OAuth, but we want to quantify that.
- As the App Store grows, will finance continue to be disproportionately complex, or will other categories (healthcare, legal) catch up as those domains start building apps?
Methodology
This analysis covers 147 third-party apps in the ChatGPT App Store as of February 2025. We excluded integrations built and maintained by OpenAI (like GitHub, Linear, Slack, and Google Workspace) to focus on apps that companies built and shipped independently.
Want access to the full dataset? Contact us to learn more.