RFC 9457 defines a standardized format for communicating error details in HTTP API responses, known as "problem details."
- Supersedes RFC 7807,
- Core fields include 'type', 'title', 'detail' and 'instance'.
- Generally paired with 4xx/5xx HTTP status codes.
- Content types `application/problem+xml`, `application/problem+json`
Example:
```
{
"type": "https://example.com/probs/invalid-input",
"title": "Invalid Input",
"status": 400,
"detail": "The provided 'age' value must be a positive integer.",
"instance": "/users/123",
"age": -5
}
```
- Supersedes RFC 7807,
- Core fields include 'type', 'title', 'detail' and 'instance'.
- Generally paired with 4xx/5xx HTTP status codes.
- Content types `application/problem+xml`, `application/problem+json`
Example:
```
{
"type": "https://example.com/probs/invalid-input",
"title": "Invalid Input",
"status": 400,
"detail": "The provided 'age' value must be a positive integer.",
"instance": "/users/123",
"age": -5
}
```
Developers are replacing bloated MCP servers with Markdown skill files — cutting token costs by 100x. This article explores a two-layer architecture emerging in production AI systems, separating knowledge from execution. It details how skills (Markdown files) encode stable knowledge, while MCP servers handle runtime API interactions. The piece advocates for a layered approach to optimize context window usage, reduce costs, and improve agent reasoning by prioritizing knowledge representation in a version-controlled, accessible format.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
This guide walks you through building production-grade MCP servers that expose your organization's internal data to AI models, covering authentication, multi-tenancy, streaming, and deployment patterns.
OpenSandbox is a general-purpose sandbox platform for AI applications, offering multi-language SDKs, unified sandbox APIs, and Docker/Kubernetes runtimes for scenarios like Coding Agents, GUI Agents, Agent Evaluation, AI Code Execution, and RL Training.
Google is announcing the public preview of the Developer Knowledge API and its associated Model Context Protocol (MCP) server. These tools provide a machine-readable gateway to Google’s official developer documentation, enabling AI assistants to access accurate and up-to-date information for building with Google technologies like Firebase, Android, and Google Cloud.
Understand API rate limits and restrictions. This document details how OpenAI’s rate limit system works, including usage tiers, headers, error mitigation strategies like exponential backoff, and batching requests.
Agoda engineers developed API Agent, a system with zero code and zero deployments that enables a single Model Context Protocol (MCP) server to connect to internal REST or GraphQL APIs. The system is designed to reduce the operational overhead of managing multiple APIs with distinct schemas and authentication methods, allowing teams to query services through AI assistants without building individual MCP servers for each API.
A collection of prompts designed to be used with AI coding assistants to build various use cases, ranging from personal CRM and knowledge bases to content pipelines and social media research.
This post breaks down why MCP servers fail, six best practices for building ones that work, and how Skills and MCP complement each other. It emphasizes designing MCP servers as user interfaces for AI agents, focusing on outcomes, flattened arguments, clear instructions, curation, discoverable naming, and pagination.
* **Focus on Outcomes, Not Operations:** Instead of exposing granular API endpoints as tools, create high-level tools that deliver the *result* the agent needs.
* **Flatten Arguments:** Use simple, typed arguments instead of complex nested structures.
* **Instructions are Context:** Leverage docstrings and error messages to provide clear guidance to the agent.
* **Curate Ruthlessly:** Limit the number of tools exposed and focus on essential functionality.
* **Name Tools for Discovery:** Use a consistent naming convention (service_action_resource) to improve discoverability.
* **Paginate Large Results:** Avoid overwhelming the agent with large datasets; use pagination with metadata.
* **Focus on Outcomes, Not Operations:** Instead of exposing granular API endpoints as tools, create high-level tools that deliver the *result* the agent needs.
* **Flatten Arguments:** Use simple, typed arguments instead of complex nested structures.
* **Instructions are Context:** Leverage docstrings and error messages to provide clear guidance to the agent.
* **Curate Ruthlessly:** Limit the number of tools exposed and focus on essential functionality.
* **Name Tools for Discovery:** Use a consistent naming convention (service_action_resource) to improve discoverability.
* **Paginate Large Results:** Avoid overwhelming the agent with large datasets; use pagination with metadata.
