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# Introduction

Code Context Engine (CCE) is a local MCP server that indexes your codebase so AI coding agents search for relevant code instead of reading entire files.

## The problem

Section titled “The problem”

Every time an AI agent needs to understand your code, it reads entire files. A 500-line file costs 500 lines of input tokens even when the agent only needs one function. Across a session, this adds up to thousands of wasted tokens and real dollars.

## The solution

Section titled “The solution”

CCE parses your code into semantic chunks (functions, classes, modules) using Tree-sitter, stores them with vector embeddings, and serves only the relevant pieces when the agent asks a question.

Result: 94% input token savings, reproducibly benchmarked.

## What you get

Section titled “What you get”

Tool | Purpose
--- | ---
`context_search` | Hybrid vector + keyword search with graph expansion
`get_chunk` | Retrieve a specific chunk by ID
`record_decision` | Store architectural decisions for cross-session recall
`record_code_area` | Mark areas you’ve worked on
`session_recall` | Recall decisions and code areas
`session_timeline` | Browse tool call history
`session_event` | Inspect a specific past event
`set_output_level` | Control output compression (off/lite/standard/max)
`set_scope` | Limit search to specific directories

## Supported agents

Section titled “Supported agents”

Editor | Config written | Instructions
--- | --- | ---
Claude Code | `.mcp.json` | `CLAUDE.md`
VS Code / Copilot | `.vscode/mcp.json` | `.github/copilot-instructions.md`
Cursor | `.cursor/mcp.json` | `.cursorrules`
Gemini CLI | `.gemini/settings.json` | `GEMINI.md`
OpenAI Codex | `~/.codex/config.toml` | `AGENTS.md`
OpenCode | `opencode.json` |
Tabnine | `.tabnine/agent/settings.json` | `TABNINE.md`

## How it works

Section titled “How it works”

1. Index — Tree-sitter parses code into semantic chunks. Stored locally with vector embeddings.
2. Search — Agent calls `context_search` via MCP. Hybrid vector + BM25 merged with Reciprocal Rank Fusion. Graph expansion adds related imports.
3. Compress — Chunks are compressed (truncation or LLM summary with Ollama). Session-wide output compression rules in instruction files reduce reply tokens (diff-only code, no filler).
4. Track — Every query recorded. `cce savings` shows tokens and dollars saved.