Requestly

How many engineering hours is your team burning chasing "phantom bugs" that are actually just header mismatches or expired tokens? Here are 6 common API debugging mistakes—and how to fix them to build a resilient, professional workflow: 👉 Trusting code over network traces: What you coded isn't always what transmits across the wire. Always verify the actual network trace to see exactly what is leaving the browser. 👉 Treating non-200 responses as generic failures: Proactively mock 400 and 500-level HTTP status codes locally, and build custom front-end logic so your app reacts intelligently when APIs fail. 👉 Relying on hardcoded hacks to test edge cases: Use a lightweight interceptor to automatically swap headers, edit request bodies, or mock API responses on the fly without touching your backend. 👉 Testing for latency only in production: Throttle specific endpoints locally to simulate server timeouts and validate your loading states and error boundaries. 👉 Breaking workflows with static data: Automate your data flow using dynamic environment variables that pass state between requests, ensuring your payloads are always contextually accurate. 👉 Manually calculating complex headers: Use pre-request scripts to automatically generate complex hashes, tokens, and dynamic timestamps so every outbound request is cryptographically valid.

When your API client and your IDE don't share a state, your API tests silently break and your team wastes hours communicating minor field changes. 👇 In this demo, Requestly Founder Sachin Jain shows exactly how "Local Projects" solve this through two-way sync. Here is what next-gen API management looks like: • One-to-One Mapping: Your API collections map perfectly to directories in your local repository • Instant IDE Sync: Add a query parameter in the Requestly UI, and it instantly updates the metadata in your IDE • Instant UI Sync: Edit your code directly in the IDE, and the Requestly app UI updates automatically 💡 Co-locate your API definitions directly within your codebase. When your API client and code share a single source of truth, "drift" becomes technically impossible. 👉 Watch the full video to see Sachin walk through the demo: https://lnkd.in/eXdTbxq7 👉 Try Requestly: https://lnkd.in/dSRP-a4J

The API tooling industry is broken, and it’s time we talked about it. Most legacy API clients are cloud-first by design. As a result, developers are dealing with leaked secrets, fragmented workflows, disconnected testing, and AI coding agents that lack the context they need to be effective. At #Breakpoint2026, Sachin Jain (CEO of Requestly and Group PM at BrowserStack) unpacked the five biggest frictions slowing modern API development: - Privacy Risks - Context Switching - No Single Source of Truth - Blind AI Agents - Isolated Testing In this BrowserStack Unpacked session, Sachin shares why the current API tooling stack is holding teams back and demonstrates how Requestly is reimagining API development with a privacy-first, local-first approach. 👉 Watch the session on demand to see a live demo of Requestly’s next-generation API Client and AI-powered API Test Authoring Agent: https://lnkd.in/eXdTbxq7 👉 Try Requestly today and experience a faster, privacy-first way to build, test, and collaborate on APIs: https://lnkd.in/dSRP-a4J

Heard anyone say #MCP is going to replace your #REST APIs? That's a category error REST is built for human developers writing deterministic code, while MCP acts like a "USB-C port" for AI agents making probabilistic decisions on the fly. Over 5,000 community MCP servers exist today, so ignoring it is a mistake—but treating it as a REST killer fundamentally misunderstands the architecture Here is the honest breakdown of how they actually coexist in a modern API stack ✅ The core difference: REST is stateless and relies on design-time discovery via OpenAPI specs. MCP is session-based and relies on runtime discovery, allowing an AI to dynamically ask your system, "What tools do you have right now?" ✅ Different callers: REST exposes standard resources and operations. MCP exposes tools, read-only data, and prompts with descriptions explicitly written for an LLM to read and execute. ✅ MCP wraps REST: MCP isn't a replacement; it's a thin wrapper. Your existing REST API remains the absolute source of truth for your business logic, validation, and auth. ✅ The translation layer: The MCP server sits on top, converting an AI agent's unpredictable tool choices into the standard, repeatable REST calls your system already understands. 💡 You don't need to rewrite your backend to support AI agents. Since MCP servers are just translation layers, you can auto-generate an MCP wrapper directly from your existing OpenAPI specs. 👇 Check out the full video breakdown to see the 5 concrete differences side-by-side and learn how to implement them!

In 2026, an #API gets you developers, but an #MCP server gets you #AIagents—and agents are your new customers. If your SaaS isn't natively accessible to tools like Claude or Cursor, those agents will simply recommend the competitor who already shipped an MCP server. Here is why shipping one is a necessary distribution play: ✅ It's the new SEO: Agents recommend the tools they can actually use. The first platform in your category with an MCP integration becomes the default answer. ✅ 80% of the work is already done: If you have a stable REST API, your auth, rate limits, and business logic are already handled. Your MCP server just reuses it. ✅ Fast Time-to-Market: You can wrap your top three workflows into MCP tools in a single 1-2 week sprint. 👇 Watch the full breakdown on the real dev effort, and the 4 specific situations where you should NOT build an MCP server yet

Throwing raw ChatGPT at your #API for test generation is a recipe for hallucinated status codes and false assertions. We spent two weeks testing 7 AI test case generators against a real OpenAPI spec so you don't have to—and these 3 are actually worth an engineering team's time: Keploy 🐰 (Grounds tests in real traffic) testRigor (Turns plain English into runnable scripts) Postman AI Agent Builder (Lowest friction for Postman users) Watch the full 5-minute teardown! 👇 👉 Bonus: If you just need a quick win, Requestly's built-in AI test generator lets you use an actual API response to generate assertions for the whole endpoint in one single pass. Try it for free: https://lnkd.in/dSRP-a4J

Disorganized API setups don't just look messy—they double your debugging time, lead to inconsistent testing, and let critical edge cases slip through Here is how to structure your API testing workflow so it actually scales. 👇 - Keep ONE base request per endpoint: Think of this as your clean, canonical template. Never modify it directly - Use a parent-child structure: Group related scenarios under the base endpoint so your sidebar becomes a structured, navigable map - Name scenarios descriptively: Ditch "test1" or "edge case". Name them exactly after what is being tested, like "Missing token – 401" or "Payload too large – 413" - Save responses alongside requests: Don't just save the request configuration. Saving the actual response creates a living reference that saves enormous time when debugging 💡 Tip: Stop mixing environments! Use variables (like ) to switch dynamically, or create clearly labeled scenario groups for staging vs. production. Mixing them up is one of the most common sources of confusing test results 👉 Read the full blog to learn the complete scenario-based testing framework and see how Requestly's Examples feature automates this structure for you: https://lnkd.in/dTZQCxQw

Your #API request folder looking like: final_v2_ACTUAL_final_use_this_FIXED.json Yeah. We built something for that. Requestly Examples lets you save every version of a request — valid response, edge case, error state — clean, named, and ready to switch between instantly. Learn more → https://lnkd.in/g-3KRhbf

Your API returns a 200 OK. Your users trying to check out say otherwise. That’s the problem with relying purely on UI tests. 👇 Here’s a 100-second breakdown on how to catch failures before they turn into 3 a.m. pager alerts. Run your API tests locally and inside your CI pipeline before every single deploy. Try Requestly for free (no login required): https://lnkd.in/dSRP-a4J