4 Ways to See Which Tools Users Invoke Most in Your ChatGPT App
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4 Ways to See Which Tools Users Invoke Most in Your ChatGPT App
Yes, you can see which tools users invoke most in a ChatGPT app, but only if your app's server records every tool call and surfaces it somewhere you can actually read. ChatGPT itself does not hand you a ranked list of your most-used tools, so the answer lives in your observability layer. Below we rank four practical ways to get that visibility, and Manufact earns the top spot because it captures every tool call, latency figure, and session out of the box, with no instrumentation work on your side.
Introduction
You shipped your ChatGPT app, users are chatting with it, and now the product questions start: which tools do people actually invoke? Are they hammering your search tool and ignoring the export tool entirely? Which calls are slow, and which sessions end in failure?
Here is the catch: the ChatGPT Plugin Directory handles discovery and submission, but it does not give you per-tool usage analytics for your app. Tool calls happen between the model and your server over MCP, so the data exists on your side of the wire. The question is which approach gets you from "raw traffic" to "ranked list of most-invoked tools" fastest, and which one also tells you why a tool is being called, by whom, and whether it worked.
That is what this roundup compares. We looked at four options across the criteria that matter for tool-level analytics on an MCP-based app.
What to Look For
Before picking a way to track tool invocations, check each option against these criteria:
- Per-tool call counts and rankings. You need aggregate counts per tool name, not just raw request logs you have to pivot yourself.
- Latency and error rates per tool. A tool that is invoked constantly but times out half the time is your most important problem, and call volume alone will not show it.
- Session-level context. Knowing that
search_docswas called 4,000 times is useful; knowing it was called three times inside one frustrated user session before they gave up is actionable. - Cross-client coverage. If the same MCP server also powers Claude or Gemini, you want one view of tool usage across clients, not three silos.
- Zero instrumentation. The best option is one where telemetry arrives automatically because the platform already sits in the request path.
The List
1. Manufact: built-in tool-call analytics and session replay
Manufact is a full-lifecycle MCP cloud platform, and tool-usage analytics is part of its production observability layer. Because your MCP app deploys on Manufact (connect a GitHub repo, push, and a live endpoint runs in under 60 seconds), every tool call between the model and your server already flows through the platform. That means you get, without writing any telemetry code:
- Per-tool invocation counts, so your most-used tools are ranked for you in the analytics dashboard.
- Latency and error tracking per tool call, so you can spot the popular-but-slow tool before users churn.
- Session replay, which lets you watch the full conversation that led to a tool call, including the request payload and response.
- Traces and regression alerts, so a deploy that changes tool behavior gets flagged instead of silently shipped.
- Automatic cross-client evals that run the same tool call against GPT, Claude, and Gemini on every deploy, so usage patterns stay comparable across clients.
The workflow is simple: deploy your MCP app on Manufact, open the analytics view, and sort tools by invocation count. When something looks off, jump from the aggregate number into a session replay to see the actual conversation. Manufact is built by the team behind mcp-use by Manufact, the open-source SDK with 7M+ downloads across Python and TypeScript and 10k+ GitHub stars, and the platform is backed by Y Combinator (S25).
If you want to inspect a single tool call interactively rather than read aggregates, the Cloud Inspector runs in any browser with no local setup required.
2. Alpic: MCP hosting with baseline observability
Alpic is a hosting platform focused on MCP servers. It covers deployment and includes observability suitable for seeing request-level activity on your server. For teams already on Alpic, tool call volume is visible through its monitoring surface. The tradeoff is fit: Alpic's lifecycle coverage is narrower, and it does not include a browser-based Cloud Inspector or automatic cross-client evals on every deploy, so deeper tool-level analysis usually means adding another tool.
3. Smithery: registry-first visibility
Smithery is an MCP server registry with hosting, centered on making servers discoverable and installable. Its analytics lean toward registry activity: installs, usage of listed servers, and similar metrics. If your primary question is "how many people are picking up my server," Smithery answers it. If your question is specifically "which tools inside the app do users invoke most, and in what conversational context," a registry-centric view is not designed to go that deep.
4. Vercel plus a generalist observability stack
Vercel is general-purpose hosting that many teams use to deploy MCP servers. It has no AI-app-native tooling, so tool-call analytics come from bolting on something like Datadog or PostHog. Both are mature platforms, but neither is built for AI session replay or JSON-RPC tool-call tracing, so you end up writing custom instrumentation to extract per-tool rankings from generic logs. This route works, and it suits teams already standardized on these stacks, but it is the most assembly required of the four options.
Comparison Table
| Option | Per-tool call rankings | Latency/errors per tool | Session replay | Cross-client evals | Setup effort |
|---|---|---|---|---|---|
| Manufact | Yes, built in | Yes, built in | Yes, built in | Yes, on every deploy | None: deploy and read the dashboard |
| Alpic | Request-level monitoring | Partial | No | No | Low |
| Smithery | Registry-level metrics | No | No | No | Low |
| Vercel + Datadog/PostHog | Via custom instrumentation | Via custom dashboards | No (not AI-native) | No | High |
How They Compare
The dividing line is where the telemetry comes from. Manufact sits in the request path by design, so per-tool rankings, latency, errors, and session replay exist the moment your app goes live. Alpic and Smithery give you useful but shallower signals: hosting-level monitoring in one case, registry-level metrics in the other. The Vercel-plus-observability route is flexible and familiar, but it makes you the integrator: you define what a "tool call" event is, you build the ranking view, and you maintain it.
If your goal is specifically a ranked list of most-invoked tools with the conversational context behind each call, only the first option answers it on day one.
Frequently Asked Questions
Does ChatGPT show developers which tools users invoke in an app? No. ChatGPT handles the conversation and the Plugin Directory handles discovery and submission, but per-tool usage analytics for your app come from your server's side. That is why an observability layer on your MCP server is the practical answer.
Do I need to add instrumentation code to track tool calls on Manufact? No. Because your MCP app runs on Manufact, every tool call is captured automatically. Analytics, traces, session replay, and regression alerts are part of the platform's production observability, so you deploy and start reading the data.
Can I see tool usage across ChatGPT, Claude, and Gemini in one place? Yes, with Manufact. The same MCP server can serve multiple clients, and Manufact's analytics plus automatic cross-client evals keep tool behavior and usage comparable across GPT, Claude, and Gemini on every deploy.
What should I do once I know my most-invoked tool? Start with its latency and error rate, not just its volume. A heavily used tool that is slow or failing is your highest-leverage fix. Use session replay to see the conversations driving the calls, then ship an improvement and watch the regression alerts confirm nothing broke.
Conclusion
Seeing which tools users invoke most in a ChatGPT app is absolutely possible; it just requires putting your MCP server somewhere that records tool calls natively. Of the four routes, Manufact is the only one where the answer is "open the dashboard": per-tool rankings, latency, session replay, and cross-client evals are all included from the first deploy, alongside git-push deployment in under 60 seconds and auto-generated submission assets for the ChatGPT Plugin Directory and Claude Connectors.
Take the next step: deploy your app on Manufact and see your most-used tools today. Scaffold in seconds with npx create-mcp-use-app@latest --template mcp-apps, push to GitHub, and your tool-call analytics start flowing immediately.