Context Plugins – API context for AI coding assistants

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Hi, we're Sohaib and Hannan from APIMatic. We built Context Plugins: given an OpenAPI spec, we generate SDKs and an MCP server that exposes structured API context to AI coding assistants. This gives tools like Cursor access to comprehensive, up-to-date API context (including SDK documentation and API integration patterns), instead of relying on outdated training data or code scraped from GitHub. We've just launched a pilot with PayPal, it's live on the PayPal Developer Portal https://developer.paypal.com/serversdk/java/getting-started/... . In our benchmarks for the PayPal API, Cursor generated integration code 2x faster with 65% lower token usage compared to baseline Cursor without the plugin. The problem we kept seeing was AI coding assistants generating incorrect API integration code. We asked Cursor to integrate the PayPal API into an e-commerce application across multiple runs: - 13% of runs pulled in a deprecated SDK. - 87% generated HTTP calls based on deprecated documentation. API providers maintain API and SDK docs, but AI assistants don’t always use them. They use a combination of web search, training data and hallucinations to write API integration code. As a result, developers end up debugging and rewriting AI-generated code. We've been generating SDKs from API specs for 10+ years at APIMatic. When AI coding assistants started generating the exact kind of broken integration code we'd spent a decade fixing, we asked: what if we could take our code generation pipeline and add AI context generation to it? MCP gave us the transport layer. Our API spec parsing and SDK generation engine gave us the context. The combination meant we could deliver deterministic API context, derived from the canonical spec, directly into the developer's IDE. Here's how it works: 1. An API provider uploads their OpenAPI spec to APIMatic. 2. We generate and publish high quality SDKs in multiple languages. 3. We generate an MCP server containing tools and prompts that expose language-specific SDK context, optimized for LLMs. 4. Developers wanting to integrate with the API install the MCP server in Cursor, Claude Code, or GitHub Copilot. When a developer asks to integrate auth, the coding assistant queries the MCP server, retrieves the required context (auth flows, integration patterns, latest SDK version, SDK interfaces etc.), and generates code using the official SDK. Getting started: - We've published Context Plugins for ten APIs. The URL in this post takes you to our public showcase, which is the quickest way to try them out without signing up. - If you want to generate Context Plugins from your own API, you can sign up for a free trial on the APIMatic website (2-week all-access trial, no credit card required). Feedback: We'd love for you to try out Context Plugins and give us your feedback and suggestions. Two questions for the HN community: - What's the worst AI-generated API integration bug you've encountered? We're collecting failure patterns to improve our context coverage. - What context do you think coding assistants need to generate accurate API integration code, in addition to API or SDK contracts?

Visit product View on Hacker NewsMar 26, 2026

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

99%99% 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 · Strong signals: started, including · Missing: supports, reddit linkedin, podcasting

96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

60%60% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% predicted probability of success on Acquire.com, 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.

Correct prediction on native model

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