The Gentoro Blog
Why Do MCP Tools Behave Differently Across LLM Models?
Why do MCP Tools behave differently across LLM models? Learn how model assumptions impact tool usage, and explore practical strategies to improve reliability.
Why Traditional Regression Testing Doesn’t Work for MCP Tools
Traditional regression testing fails for modern AI systems powered by MCP. Learn how to rethink testing for non-deterministic LLM-driven workflows.
Why Enterprise Systems Aren’t Ready for AI Agents (Yet)
Enterprise systems aren’t ready for AI agents. From integration bottlenecks to outdated infrastructure, here's why adoption stalls and how Gentoro can help.
From Connection Chaos to Intelligent Integration
Gentoro is now GA! Use Gentoro to go from jury-rigging together APIs to vibe-coding production-ready, fully usable MCP Tools.
Building MCP Tools: From Protocol to Production
Building MCP Tools for AI agents isn’t easy. Learn how Gentoro's vibe-based approach simplifies MCP Tool generation.
Connecting Agents to the Enterprise With MCP Tools
Most APIs aren’t built for AI agents. Learn how MCP Tools provide a secure, intent-based interface that bridges LLMs and enterprise systems.
How MCP Tools Bridge the Gap Between AI Agents and APIs
Most APIs aren’t built for AI agents. Learn how MCP Tools provide a secure, intent-based interface that bridges LLMs and enterprise systems.
How MCP Leverages OAuth 2.1 and RFC 9728 for Authorization
Learn how the Model Context Protocol (MCP) adopts OAuth 2.1 and RFC 9728 to enable dynamic, secure authorization for AI agents and agentic tools.
Turn Your OpenAPI Specs Into MCP Tools—Instantly
Introducing a powerful new feature in Gentoro that lets you automatically generate MCP Tools from any OpenAPI spec—no integration code required.
Navigating the Expanding Landscape of AI Applications
Learn how developers are navigating the complex landscape of LLM apps, AI agents, and hybrid architectures — and how protocols like MCP and A2A are shaping the future of AI integration.
Deploying a Production Support AI Agent With LangChain and Gentoro
Deploy an AI agent using LangChain and Gentoro to automate incident detection, analysis, team alerts, and JIRA ticket creation, enhancing production support efficiency.
Announcing: Native Support for LangChain
Build production-ready AI agents faster with Gentoro’s native LangChain support—no more glue code, flaky APIs, or auth headaches.
What Are Agentic AI Tools?
Agentic tools let AI act beyond text generation, using inferred invocation to interact with real-world applications. Learn how MCP simplifies AI-tool connections.
LangChain: From Chains to Threads
LangChain made AI development easier, but as applications evolve, its limitations are showing. Explore what’s next for AI frameworks beyond chain-based models.
Vibe Coding: The New Way We Create and Interact With Technology
Vibe coding, powered by generative AI, is redefining software creation and interaction. Learn how this paradigm shift is transforming development and user experience.
Rethinking LangChain in the Agentic AI World
LangChain is powerful but manually intensive. What if agentic AI handled the complexity? Explore how directive programming could redefine AI development.
Introducing Model Context Protocol (MCP) Support for Gentoro
Discover how Gentoro’s support for Model Context Protocol (MCP) simplifies AI tool integration, enabling smarter workflows with Claude Desktop and more.
LLM Function-Calling vs. Model Context Protocol (MCP)
Explore how function-calling and MCP revolutionize enterprise workflows by simplifying LLM usage and showcasing their unique roles in development.
Using MCP Server to Integrate LLMs Into Your Systems
Learn how MCP servers streamline enterprise LLM development, overcome framework hurdles, and power scalable, efficient generative AI applications with ease.
LLM Function-Calling Performance: API- vs User-Aligned
Discover how API-aligned and user-aligned function designs influence LLM performance, optimizing outcomes in function-calling tasks.
Building Bridges: Connecting LLMs with Enterprise Systems
Uncover the technical hurdles developers encounter when connecting LLMs with enterprise systems. From API design to security, this blog addresses it all.
Contextual Function-Calling: Reducing Hidden Costs in LLM Function-Calling Systems
Understand the hidden token costs of OpenAI's function-calling feature and how Contextual Function-Calling can reduce expenses in LLM applications.
Simplifying Data Extraction with OpenAI JSON Mode and Schemas
Discover how to tackle LLM output formatting challenges with JSON mode and DTOs, ensuring more reliable ChatGPT responses for application development.
Why Function-Calling GenAI Must Be Built by AI, Not Manually Coded
Learn why AI should build function-calling systems dynamically instead of manual coding, and how to future-proof these systems against LLM updates and changes.
User-Aligned Functions to Improve LLM-to-API Function-Calling Accuracy
Explore function-calling in LLMs, its challenges in API integration, and how User-Aligned Functions can bridge the gap between user requests and system APIs.
Function-based RAG: Extending LLMs Beyond Static Knowledge Bases
Learn how Retrieval-Augmented Generation (RAG) enhances LLMs by connecting them to external data sources, enabling real-time data access and improved responses.
Customized Plans for Real Enterprise Needs
Gentoro makes it easier to operationalize AI across your enterprise. Get in touch to explore deployment options, scale requirements, and the right pricing model for your team.
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News & Articles
Gentoro Announces General Availability of MCP Tools Platform to Accelerate Connecting AI to the Enterprise
Gentoro today announced the general availability (GA) of its enterprise-grade platform built to accelerate the development and deployment of AI agents in real-world environments.
