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Add visualisations to your data MCP in a few lines of code

Hacker News

Add visualisations to your data MCP in a few lines of code

Our small company works in the data space, and the shift towards agent-friendly data surfaces is the big thing right now. We decided to get stuck in by learning how to build MCPs and semantic layers, and then testing them with our clients. To be honest, we wouldn’t say this is too challenging for a smart data engineer or analyst with a good agent client. However, adding charts (as an MCP app) and writing the front-end code was a pain. Trying to make these agent-friendly was also a pain. After lots of trial and error, we’ve put something fairly effective and wanted to share it with the wider data community. If you’re building MCPs on top of any sort of data, then give it a try. We’re always open to feedback

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, mcp, visual · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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