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AI agents are bad at API integrations

we fixed it

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Hi, we're Sohaib and Hannan from APIMatic. We've been building tools to help Developers integrate with APIs for 5+ years at APIMatic. We're now trying to help AI agents do the same.This started from a conversation at PayPal DevDay 2025. The PayPal developer experience team were monitoring developers using AI agents to integrate PayPal APIs, and the agents kept reaching for outdated docs and deprecated SDK versions, often falling back to training data instead of reading the current API documentation.We tested this ourselves with Cursor. Across multiple iterations, 87% of runs fetched outdated reference docs using web search, and 13% of implementations used a deprecated SDK version.What we built:We built Context Plugins - given an OpenAPI spec, we generate SDKs and a remote MCP server that exposes language-specific API context (auth flows, method signatures, error handling, pagination, integration patterns from the API team) packaged and optimised for LLM consumption. Instead of guessing from training data or relying on web search, coding agents get the correct SDK methods and integration patterns they need to build API integrations.We've just launched in Beta with PayPal: https://developer.paypal.com/serversdk/java/getting-started/...We benchmarked Context Plugins on two codebases, nopCommerce (a mature ASP.NET e-commerce platform) and Microsoft's eShop reference app, and saw 2x faster integration time, 65% lower token usage, and near-zero hallucinations.Getting started:- We've published ready-to-use Context Plugins for Spotify, Twilio, Slack, and other APIs; the Show HN link takes you to our product showcase with one-click install for popular AI agents.- Want to generate Context Plugins from your own spec? Sign up for a free 2-week trial on the APIMatic website, no credit card required.- Want to dive deeper into the data? Read more here https://www.apimatic.io/product/context-plugins/case-studyWe're really looking forward to hearing the community's feedback on this. We're particularly interested in hearing from API platform teams seeing this problem with their users, developers who've fought this problem manually or anyone who's tried building something similar.What are we missing? What would make this more useful?

HN · launched Apr 9, 2026 · 6 upvotes (top 72%) · 0 views

Niche: ai agent development platforms · 598 launches, 22 in the last 7 days ▼ -29% · Explore this niche →

Category and tags

  • Horizontal
  • Dev tools
  • Developer
  • AI-native
  • Commercial product

Visit website ↗ Source listing ↗

Could you build this?

Partial While presenting API definitions to LLMs via MCP or tool schemas is simple, creating an enterprise-grade automated agent integration platform requires deep spec normalization, synthetic sandbox generation, and multi-step validation.

What it would actually take: The stack would involve an ingestion engine parsing complex OpenAPI/GraphQL/gRPC schemas, generating deterministic context-minimized tool calls, running automated contract testing sandboxes, and orchestrating fallback mechanisms. The hard part is managing large, messy real-world APIs and dynamically pruning API specs into context windows without losing crucial parameter dependencies.

Discussion

3 comments analyzed.

Competitors mentioned: OpenAPI Spec, API Reference documentation, Web search for API docs

Concerns raised: Handling APIs with aggressive versioning and breaking changes across major versions, Maintaining accuracy when supporting multiple API versions simultaneously, Agents falling back to outdated docs or SDKs

Feature requests: Language-specific context structured around SDK methods, Built-in handling of auth, errors, pagination, and rate limits in context

Competitors

Other products that read as similar to this one : 521 launches clear the similarity bar, closest 8 shown.

Attention rank: #451 of 522 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 162 days after the earliest competitor.

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