Here is a complete, structured list of the capabilities / “tools” OpenAI currently provides for agents (through ChatGPT and the API), summarized by OpenAI itself: 1. Reasoning & General Models GPT models (GPT-4o, GPT-4.1, GPT-o3, GPT-o1, Codex variants) Text, code, multimodal reasoning (text, images, audio). Used as the “core brain” of agents. 2. Code & … Continue reading OpenAI’s Offering to Assist Engineers to Build Agents

Existing Findings: Rigid flow in SimpleAgent.handle_user_request(): Heavy branching between categorization, correction checks, and confirmation loops forces users into strict yes/no responses and long prompts before running tools. Tool confirmation friction: The confirmation block within SimpleAgent.handle_user_request() expects precise inputs; any deviation reruns prompts rather than gracefully extracting intent. LLM prompt templates: Prompts in SimpleAgent.generate_intelligent_analysis() and SimpleAgent.select_tool_for_request() include verbose, formal instructions lacking conversational tone … Continue reading Leveraging OpenAI’s Agents to Improve My Agent

Here is the comprehensive understanding of all 19 MCP tools. Here's the complete analysis: 📋 Complete MCP Tool Inventory (19 Tools) NSS Tools (4 tools): nss_analyze - News search analysis with entities, keywords, date ranges nss_market_data - Add market data to NSS results nss_composite_score - Calculate composite keyword scores nss_full_pipeline - Complete NSS workflow RBICS … Continue reading Existing MCP Tools

The report is structured into three levels: Tactical, Operational, and Strategic. Effective LLM development relies on clear prompting strategies, structured design, and robust evaluation. Techniques such as n-shot in-context learning, chain-of-thought reasoning, and well-chosen examples improve model guidance, while schemas, specifications, and metadata ensure consistent inputs and outputs. Breaking tasks into smaller prompts helps models … Continue reading What We’ve Learned From a Year of Building with LLMs by Eugene Yan et al

Even though it's raw, this quant assistant agent I built can be compared to current agents like Claude (Anthropic), Cursor, and Windsurf. High-level summary Your agent is a focused, developer-controlled ReAct loop with MCP tooling and a local code-exec sandbox. It’s closer to a programmable research/analysis assistant than a full IDE agent. Compared to Claude/Cursor/Windsurf, … Continue reading Quant Assistant Agent Comparison to Robust Agents in Market

Money, wealth, and prosperity—whether for a person or a nation—are not tangible assets in the way we often imagine. They're fundamentally created from "thin air," or more accurately, from credit and debt. The core principle is simple: money is a promise of future value. When a bank grants a loan, it's not simply handing you … Continue reading How to Make Money by a Country and an Individual

The U.S. national debt is the cumulative total of the federal government's annual budget deficits. It's a direct result of the government consistently spending more than it collects in taxes. This debt, in the form of Treasury securities, finances a wide range of government functions and programs, including social security, Medicare, defense, and infrastructure. During … Continue reading U.S. vs. China Debt and How Fed and Central Bank Play the Role

The current setup uses the Model Context Protocol (MCP) which is: Request-Response based: Each tool call is a single request that returns a complete response JSON-based messaging: Structured but not optimized for streaming Stdio transport: Communication over stdin/stdout pipes Synchronous by nature: Even with async Python, the protocol itself expects complete responses Worth noting the system prompt … Continue reading Quant Assistant Agent First Version Summary

Good API design is about creating a predictable, secure, well-documented interface that developers can easily understand and use reliably. Focus on consistency, clear error handling, proper status codes, and comprehensive documentation Restful Design Request/response Design HTTP Status Code API Versioning Input Validation/Sanitation Error Handling Documentation Design Security Design Performance Design Testing Design

My current mcp server equipped agent can do # Current: Natural Language → Tool Selection → Results DisplayUser: "analyze my portfolio against S&P 500"Agent: [Calls pa_analyze_portfolio] → "Analysis complete! Results saved to Excel." After transformation, it will # Enhanced: Natural Language → Tool Execution → Code Generation → Advanced Analysis User: "analyze my portfolio against … Continue reading Idea of Transforming MCP Agent to a Quant Assistant!