This topic examines leading LLMs (Anthropic’s Claude Opus 4.5, Google Gemini 3, and GPT variants) for coding and agentic workflows, emphasizing tool integrations, chat API connectivity, and agent observability. As of 2025-11-26, production use of LLMs is dominated less by raw generation quality and more by their ability to reliably call, orchestrate, and observe external tools and services. Key integration patterns center on the Model Context Protocol (MCP) and chat APIs that let models access real-world capabilities: GitHub MCP Server for reading repos and managing PRs/issues; pydantic-ai’s mcp-run-python for sandboxed Python execution (Deno/Pyodide) and safe runtime testing; Playwright and Browser MCP for browser automation and scraping; TalkToFigma and Framelink MCP for programmatic design manipulation; and connectors for Supabase, AWS, Azure, Grafana, and Atlassian to handle data, infra, monitoring, and incident workflows. Kiln and Agent TARS illustrate orchestration layers that mount MCP servers to chain tools and Agent2Agent (A2A) interactions. Practical differences between models today include context-window size and multimodal inputs, latency and throughput under chat API constraints, built-in safety/guardrails, and native support for tool-calling and observability hooks. Agent observability—structured logs, traces of tool calls, deterministic replays, and permissioned access to secrets—is increasingly a primary selection criterion for teams deploying coding agents in CI/CD, incident response, and design-to-code pipelines. Choosing an LLM therefore requires balancing code-quality and reasoning with the maturity of its tool ecosystem, observability features, and available MCP/chat API integrations. The most useful setups combine a model with robust MCP servers, sandboxed execution, and monitoring integrations so agents can act, be audited, and be debugged reliably in production.