How do I install code-context-engine?

Its own README gives this command: uvx --from "code-context-engine[local]" cce init. Run it where your MCP client can launch the server, and check the README for any settings it asks for.

Which AI clients can use code-context-engine?

Its own description mentions Claude Code, Cursor and Gemini CLI. That is a mention in its description, not a test we ran.

Is code-context-engine official and is it maintained?

We found nothing naming it as a vendor’s official server. Its repository has 435 GitHub stars and its state in our directory is listed. Its last commit we recorded was on 2026-08-23.

What licence does code-context-engine use?

The repository declares the MIT licence.

InfratoApp / Resources / MCP servers / code-context-engine

code-context-engine

code-context-engine is an MCP server, listed under developer tools. Its repository has 435 GitHub stars and is licensed under MIT; we last read it on 2026-10-22.

This tool indexes codebases for AI agents to search, reducing the need to read files directly. It works with Claude Code, Codex, Copilot, Cursor, and Gemini CLI, requiring no API key for the local MCP server, which is free and open source.

Install

Read from code-context-engine’s own README, close to verbatim -- not written by us, and not guessed.

uvx --from "code-context-engine[local]" cce init

View in code-context-engine’s own README ↗

What it reaches, and what it needs

Reaches

○ not recognised

Credential

○ none found

What it connects to

Supported in

Claude Code, Cursor, Gemini CLI

Unverified means a mention in the server’s own description, not a citation. A wrong claim here costs nothing to correct and nothing to trust yet.

Maintenance

Stars

435

State

listed

Licence

MIT

From the repository

Save 94% on AI coding tokens. Index your codebase, agents search instead of reading files. Works with Claude Code, Codex, Copilot, Cursor, Gemini CLI. Local MCP server, free, open source.

View on GitHub ↗2026-10-22

What code-context-engine can do

11 tools documented in its README. An AI client calls these by name.

context_search: Hybrid vector + BM25 search with graph expansion
expand_chunk: Full source for a compressed result
related_context: Find code via graph edges (calls, imports)
session_recall: Recall decisions from past sessions
session_timeline: Walk turn summaries for a session (drill into recall hits)
session_event: Inspect raw tool input/output for a specific event
record_decision: Save a decision for future sessions
record_code_area: Record which files were worked in
index_status: Check index freshness
reindex: Re-index a file or the full project
set_output_compression: Adjust response verbosity (`off` / `lite` / `standard` / `max`)

Read from the README; names are kept only if the README contains them.