Opportunities › Product analytics for AI agent tool integrations › Proposed app
Toolprint
See how AI agents use your MCP tools, where they fail and what users want.
- Score
- 49
- confidence 61
- Evidence
- 13
- items on the page
- Rivals
- 2
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2025
- New market · AI estimate
ScreensConcept
Desktop app: Engineering teams analyze dense session reconstructions, failure digests, and dashboards while working long stretches at their desks to understand and debug agent behavior. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Use-cases
Last 7 days14 clusters
About this appAI proposal
Toolprint is product analytics for teams that expose tools to AI agents through MCP. Add a small SDK to your server and it rebuilds each agent session from the tool calls: what the user seemed to want, which calls the agent made and in what order, and how it ended.
It groups sessions into use-cases on its own and lists the most frequent failure modes, such as wrong arguments, invented IDs or retry loops. You see which workflows matter instead of guessing.
Any failing session can be saved as a test case. Replay it against your next release so a fix is checked on real agent behaviour, not only on cases you wrote by hand.
- Tagline
- Know what agents do with your tools before your users tell you.
- Voice
- ClearPracticalCalm
- Why this name
- A fingerprint-like trace left by each agent's tool use. Two plain words joined, easy to say and spell. Trademark check needed.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Three-line SDK setup
Wrap your MCP server once. Calls are captured with little overhead and no changes to your tool code.
No member yet
Session reconstruction
Tool calls are stitched into sessions showing inferred intent, call order, arguments, results and outcome.
No member yet
Automatic use-case clusters
Sessions are grouped by what users were trying to do, so you see which workflows get the most traffic.
No member yet
Failure mode digest
Repeated errors, malformed arguments and made-up identifiers are grouped and ranked by how often they happen.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F6F6BDeep Teal · primary#F6F3ECPaper · surface#1E2A2BInk · text#E07A3FSignal Orange · accent#B5473AClay · data, failures#8FB09ASage · data, success
Typography
Aa
Toolprint
PricingAI proposal
The material gives no competitor prices. Tiers follow the inferred model of session volume and endpoints with a free tier; the amounts are a starting point to test.
Free
Free
- One MCP endpoint
- Low session volume
- 7-day retention
- Use-cases and failure digest
Team
$49 a month
- Up to 5 endpoints
- Higher session volume
- 30-day retention
- Risky call alerts
- Replay tests in CI
Scale
$249 a month
- Unlimited endpoints
- Highest session volume
- 1-year retention
- Redaction rules and audit log
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- No loop in the product. Reached through developer content, MCP communities and docs. Public share links for a failure report could help, but the main path is direct sales and content.
- Do they become customers?
- Viewers of shared reports are mostly teammates who already use it. Test whether SDK installs from MCP directories and posts convert from free to Team.
- First customers
- Small engineering teams at SaaS companies that run a production MCP server for their own product, with a few thousand agent sessions a month.
- Where to reach them
- Hacker News and developer forums: Post a teardown of real agent failures found in a sample MCP server, with the SDK linked.
- MCP community servers and directories: Offer a free instrumented example server and a setup guide listed alongside popular MCPs.
- Direct outreach to MCP maintainers: Message teams with public MCP servers, offering a free failure report on their traffic.
- First milestone
- Five teams with live MCP servers on the free tier, and two converting to a paid Team plan within 60 days.
Why people already pay for this
- VerifiedHacker News post received 42 points and 8 comments
- AI inferenceTeams with production MCPs and AI agent integrations have no visibility into agent behavior and user satisfaction.
- AI inferenceTesting and analytics for agent-exposed products have been persistent, unsolved pain points for teams with production deployments.
- Public claimUsers implement custom workarounds (CI triggers, custom result-fetching logic) to work around lack of native analytics.
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Event ingestionHigh-volume event store (columnar or time-series)Tool-call events arrive in bulk and need fast grouping that plain Postgres may not handle at scale.
- This app · SDKPublished TypeScript and Python packagesMCP servers are written in these languages; setup must stay at a few lines.
RisksAI inference
- AIIntent inference and clustering may be wrong or noisy, which would erode trust in the use-case view. Needs checking against sessions that teams label by hand.
- DataTool arguments and results can hold personal or sensitive data. Teams may refuse to send them, so redaction must work before launch.
- TechnicalThe SDK must add almost no latency or failures to production tool calls. Claimed overhead in the source is unverified and must be measured.
- CommercialTeams may build basic logging themselves, or existing observability vendors may add MCP views. Willingness to pay beyond the free tier is unproven.
- PlatformMCP specs and agent clients keep changing. Session boundaries may be hard to infer when clients do not pass a conversation ID.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.