#tools

ACP standardizes the boundary between coding-agent clients and coding-agent implementations: clients own the user interface and environment, agents own the model loop, and both…

The Piebald AI repository presents Claude Code as a prompt-and-tool system made of many small runtime-interpolated fragments for agents, tools, modes, classifiers, reviews…

OpenAI's Codex guide argues that Codex performance depends as much on harness prompt/tool design as on model choice: prompts should emphasize autonomy, efficient exploration…

Public coding-agent prompts converge on layered prompt assembly: base identity, behavior policy, tool/editing constraints, environment and project context, then the user's…

MCP defines a JSON-RPC lifecycle for connecting clients to external tool and context servers, with explicit initialization, capability negotiation, transport rules, tool…

Provider APIs expose similar agent concepts through different wire shapes, so a coding harness needs provider-specific adapters that normalize messages, reasoning, tool calls…