Route data analysis, large-file processing, and web retrieval through context-mode tools, returning only relevant results to the conversation. This helps preserve the context window, the limited amount of information an AI can work with at once.
MANDATORY routing rules for context-mode. Invoke whenever you analyze, count, filter, compare, search, parse, or transform data; fetch a URL; or run a data-heavy command — so raw bytes stay out of the context window.
Review the instructions the agent will receive before you install.
context-mode MCP tools are available in GitHub Copilot CLI. These rules protect the context window from flooding — one unrouted command can dump tens of KB into the conversation. Follow them strictly.
Analyze / count / filter / compare / search / parse / transform data: write
code via ctx_execute(language, code) and console.log() only the answer. Do
NOT read raw data into context. PROGRAM the analysis, do not COMPUTE it by
reading. One script replaces ten tool calls.
ctx_fetch_and_index(url, source) then ctx_search(queries).node -e "fetch(...)", python -c "requests.get(...)") — use
ctx_execute(language, code); only stdout enters context.ctx_execute_file(path, language, code).ctx_batch_execute(commands, queries) — runs all commands,
auto-indexes, returns search. One call replaces dozens.ctx_execute / ctx_execute_file — sandbox; only stdout enters
context.ctx_fetch_and_index(url, source) then ctx_search(queries) — raw
HTML never enters context.ctx_search(queries: [...]) — all questions in one call.Write artifacts to FILES; return a path + one-line description, not inline dumps.
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Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.
Route codebase exploration, file analysis, and web research through context-mode tools in Antigravity CLI. Process large outputs outside the conversation and return focused findings, with indexed content available for later searches.
Coordinate multiple agents to maintain context-mode, from sorting GitHub issues and reviewing pull requests to preparing releases and announcements. Workflows require reproduced bugs, source evidence, test-first changes, and checks across supported platforms.
Run context-mode diagnostics to check runtimes, hooks, full-text search support, plugin registration, and npm and marketplace versions. Results appear directly in the conversation with clear status labels, with a CLI fallback if the MCP call fails.
Index local files or folders in context-mode’s persistent search database, so later searches can retrieve relevant passages without rereading entire files. Useful for repeatedly consulting project documents while keeping raw file contents out of the conversation.
Open the hosted context-mode Insight dashboard in your default browser to access analytics for AI-assisted engineering teams, including productive rates per engineer, retry waste, blockers, and views tailored to different roles.