docs: update tool references for naming consistency

Author: ofriw

website/developer-tools/cli-tools.md

@@ -378,7 +378,7 @@ sudo pacman -S zellij       # Arch
 
 **Key differentiators:** Ref-based accessibility tree system returns compact snapshots with deterministic element references (`@e1`, `@e2`)—agents click by ref instead of fragile CSS selectors or XPath. Token-efficient output (200-400 tokens per snapshot vs 5,000-15,000 for full DOM) preserves agent context window. 50+ commands cover navigation, forms, screenshots, network inspection, and storage. Session support enables multiple isolated browser instances with separate authentication states. Native Rust CLI provides instant command parsing without Node.js or Python runtime overhead.
 
-**Best suited for:** AI-assisted workflows where agents need to interact with web UIs—testing changes in browser, filling forms, extracting data, validating deployments. Engineers using CLI-based agents (Claude Code, Cursor, Copilot) who need browser automation without MCP server setup. Developers wanting deterministic element selection over screenshot-based visual parsing or brittle selector strategies.
+**Best suited for:** AI-assisted workflows where agents need to interact with web UIs—testing changes in browser, filling forms, extracting data, validating deployments. Engineers using CLI-based agents (Claude Code, Cursor CLI, Copilot CLI) who need browser automation without MCP server setup. Developers wanting deterministic element selection over screenshot-based visual parsing or brittle selector strategies.
 
 **Trade-offs:** Ref-based selection requires snapshot before interaction (two commands minimum). Relies on accessibility tree, which may miss dynamically rendered content without proper ARIA attributes—ensure target applications have semantic markup.
 

website/developer-tools/mcp-servers.md

@@ -5,7 +5,7 @@ sidebar_position: 4
 
 # MCP Servers
 
-The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) extends CLI agents with specialized capabilities—code research, web grounding, browser automation. While IDE-based assistants (Cursor, Windsurf) often include these features built-in, CLI agents (Claude Code, Copilot CLI, Aider) rely on MCP servers to add functionality beyond basic file operations.
+The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) extends CLI agents with specialized capabilities—code research, web grounding, browser automation. While IDE-based assistants (Copilot's agent mode in VS Code, Zed, Cursor) increasingly ship these capabilities, CLI agents (Claude Code, Copilot CLI, Aider) rely on MCP servers to add functionality beyond basic file operations — MCP is now the shared extension standard across both surfaces.
 
 These MCP servers address the critical gaps in AI-assisted development workflows.
 

website/docs/context-engineering.mdx

@@ -105,7 +105,7 @@ Context files are markdown documents injected between the system prompt and your
   <ContextSqueezeDiagram />
 </DiagramFrame>
 
-`AGENTS.md` is the vendor-neutral standard adopted by 60,000+ open-source projects (now governed by the Linux Foundation's Agentic AI Foundation), working across GitHub Copilot, Cursor, Windsurf, and most other AI coding tools (note: Claude Code does not support AGENTS.md — see tip below). Keep it minimal — your README should contain 90% of what AI needs; AGENTS.md adds only AI-specific operational context. That 10% is where the discipline lives — every token in AGENTS.md sits in the attention valley between the prefix and your actual task. Put project knowledge in your README where agents can read it on demand; AGENTS.md is for what changes _how_ the agent operates, not _what_ the project does. Reference external docs by link rather than inlining them — agents can fetch details when needed.
+`AGENTS.md` is the vendor-neutral standard adopted by 60,000+ open-source projects (now governed by the Linux Foundation's Agentic AI Foundation), working across GitHub Copilot, Cursor, Zed, Windsurf, and most other AI coding tools (note: Claude Code does not support AGENTS.md — see tip below). Keep it minimal — your README should contain 90% of what AI needs; AGENTS.md adds only AI-specific operational context. That 10% is where the discipline lives — every token in AGENTS.md sits in the attention valley between the prefix and your actual task. Put project knowledge in your README where agents can read it on demand; AGENTS.md is for what changes _how_ the agent operates, not _what_ the project does. Reference external docs by link rather than inlining them — agents can fetch details when needed.
 
 :::tip Claude Code: Using AGENTS.md with Claude Code
 Claude Code uses `CLAUDE.md` instead of `AGENTS.md`. To maintain a single source of truth while supporting both ecosystems, use **@-linking** in your `CLAUDE.md`:

website/docs/how-agents-work.mdx

@@ -206,7 +206,7 @@ That placement changes the agent's character. A harness agent amplifies the oper
 
 ### 1. Interactive Harness Agents
 
-Interactive coding agents — Claude Code, Codex-style terminals, Pi, OpenClaw, Cursor/Copilot-style coding agents — let the LLM drive many next-step decisions through tool calls or command requests. The harness provides guardrails and execution, but the model often chooses the next search, read, edit, test, or question.
+Interactive coding agents — Claude Code, Codex-style terminals, Pi, OpenClaw, Copilot's agent mode in VS Code, Cursor, Zed — let the LLM drive many next-step decisions through tool calls or command requests. The harness provides guardrails and execution, but the model often chooses the next search, read, edit, test, or question.
 
 <DiagramFrame kicker="Agent mechanics" title="Harnesses turn model calls into controlled work" size="wide" caption="Interactive harness agents hand more autonomy to the LLM; the tradeoff is higher operator skill in scope, review, and verification.">
   <InteractiveHarnessWorkbench />