Let your agent build its own tools with MCPglue

Hacker News

We spoke to dozens of devs trying to deploy agents in prod. We found that most of them have the following dilemma (and end up not deploying because of this): giving the agents freedom to choose from a list of tools while guaranteeing that every call stays secure, fast, and actually works. That’s why we built MCPglue. It gives you the reliability you’re looking for when deploying in prod, and agents the custom tools they need to do their job well. Here’s a walkthrough of how it works: https://www.youtube.com/watch?v=uc-jdM9XnhE MCPglue lets your agent build its own tools. It can call the MCP to wrap and merge multiple endpoints from multiple APIs into coherent, reusable tools. MCPglue abstracts away endpoint mapping and API calls, and acts like a repository pattern that stays stable even as upstream APIs or mappings change. Use MCPglue to let your agent run cross-API workflows reliably, like fetching transactions in Stripe and updating them in Hubspot, to build ambient agents that react to app triggers or to wrap your own API into task-specific tools agents can use. Repo: https://github.com/superglue-ai/superglue Quickstart guide: https://docs.superglue.cloud/mcp We've seen agents handle max 10 tools at once. Would be curious to hear how many tools your agents can handle on average. And ofc what you would want to build with MCPglue.

Visit product View on Hacker NewsMay 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

95%95% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, 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

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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