Index your codebase to save 94% on AI coding tokens by enabling agents to search instead of reading files.
code-context-engine is easy to set up with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
gh repo view elara-labs/code-context-engine --webOpen the official README and confirm the supported install method.
Add the server entry to your MCP client config.
Restart your agent and verify that the server tools appear.
Repository setup guidance
Strong trust signals; still review the README and permissions before production use.
Last commit was about 110 days ago.
151 GitHub stars indicate community interest.
17 open issues signal maintenance load.
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Code Context Engine creates a searchable index of your codebase so AI coding assistants can find relevant code instantly without reading every file. This reduces the number of tokens sent to the AI, saving you up to 94% on costs. It works locally, keeps your code private, and integrates with popular tools like Claude Code, Cursor, and VS Code.
Code Context Engine (CCE) is an open-source, local MCP server that indexes your codebase and allows AI coding agents to search for relevant code context instead of reading entire files. By providing a semantic and keyword search over your code, CCE dramatically reduces the number of input tokens consumed by AI assistants, leading to up to 94% token savings as benchmarked on real-world codebases like FastAPI. CCE runs entirely on your machine with no cloud dependency, ensuring code privacy. It auto-detects your editor and works with multiple AI coding tools including Claude Code, Codex CLI, GitHub Copilot, Cursor, Gemini CLI, and more. The tool is free, MIT-licensed, and requires minimal setup—just one command to start indexing. It also supports cross-session memory, multi-editor teams, and faster response times by reducing context size.
Index your codebase to save 94% on AI coding tokens by enabling agents to search instead of reading files.
Developers using AI coding assistants who want to reduce token costs., Teams working with multiple AI coding tools who need a unified code context., Privacy-conscious developers who require local-only code indexing.
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Review repository permissions, executed commands, data sent to external services, and dependencies before use.
Similar or complementary options to evaluate include metorial, mcp-context-forge, Microsoft Learn MCP Server.
Aug 20, 2026
MIT license detected.
0 security/trust notes recorded.
Setup difficulty is 1/5.
151
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Forks
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MIT
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