エージェントを強化
公開元自身のコマンドです。PlugMCP のエンドポイントではありません。
キーとログ付きでコピーをホスト。
uvx code-context-engine==0.4.13Claude Code にはローカルコマンド用のインストールリンクがありません。下のコマンドをコピーして、ターミナルで実行してください。
claude mcp add io.github.ai-elara-code-context-engine -- uvx code-context-engine==0.4.13ターミナルで実行します。
{ "mcpServers": { "io.github.ai-elara-code-context-engine": { "command": "uvx", "args": [ "code-context-engine==0.4.13" ] } }}claude_desktop_config.json
リモート URL か、手元のマシンのコマンドか 2 つのインストール方法の違い
PlugMCP で実行
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
起動公開元より
Index your codebase. AI searches instead of re-reading files. 94% token savings.
| 94% fewer input tokens per session |
| 🔒 | Keep code private | Everything local, no cloud indexing |
| 🔄 | Multi-editor teams | One index across Claude Code, Cursor, VS Code, Gemini CLI |
| 🧠 | Cross-session memory | Decisions and context survive restarts |
| ⚡ | Faster responses | Less context = faster Claude replies |
| 📊 | Track actual savings | Dollar amounts, not estimates |
One command. 30 seconds.
uvx --from "code-context-engine[local]" cce init # install + index + configure, one shot
Or if you prefer a persistent install:
uv tool install "code-context-engine[local]" # or: pipx install "code-context-engine[local]"
cd /path/to/your/project
cce init
Restart your editor. Done. Every question now hits the index instead of re-reading files.
Agent Plugin support: Run
cce init --pluginto generate a portable Agent Plugin directory that works with VS Code, Cursor, Copilot, Codex, ChatGPT, and Kiro. The plugin usesuvxto launch CCE on demand, so users don't need to pre-install the Python package. See Agent Plugin below.
Already have Ollama? Skip
[local]and useuv tool install code-context-engineinstead. CCE auto-detects Ollama at localhost:11434 and usesnomic-embed-text.
Python 3.11+ and a C compiler (for tree-sitter grammars).
Tested on macOS, Linux, Windows with Python 3.11/3.12/3.13.
cce init auto-detects your editor and writes the right config. To target a
specific agent, use --agent claude, --agent codex, --agent copilot, --agent pi, or
--agent all.
Multiple editors in the same project? All get configured in one command.
Codex note: Codex CLI reads MCP servers from ~/.codex/config.toml only —
it has no per-project config. cce init adds one [mcp_servers.cce-<project>-<hash>]
section per project so multiple projects coexist; cce uninstall removes only
the section for the current project.
Pi note: Pi does not support MCP natively. To use CCE with Pi, you need a
pi MCP adapter extension (e.g. pi-mcp-adapter)
that consumes the .mcp.json config and exposes CCE's tools to the Pi agent.
cce init sets up both .mcp.json and AGENTS.md (Pi loads the latter
automatically for startup instructions).
my-project · 38 queries · last query 5m ago
⛁ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ 88% tokens saved
Input savings 1.9M tokens $27.78
Output savings 4.8k tokens $0.36
──────────────────────────────────────────
Total saved 1.9M tokens $28.15
Breakdown:
retrieval 84% ▰▰▰▰▰▰▰▰▰▰ 1.8M $26.76 · 12 calls
chunk compression 3% ▰▱▱▱▱▱▱▱▱▱ 68.5k $1.03 · 12 calls
output compression* <1% ▰▱▱▱▱▱▱▱▱▱ 4.8k $0.36 · 12 calls
Cost estimate based on Opus pricing (input $15.0/1M, output $75.0/1M)
Supports Anthropic, OpenAI, and Google model pricing. Configure via pricing.model in ~/.cce/config.yaml.
Input tokens are 85-95% of your Claude Code bill. CCE cuts them by 94% (benchmarked on FastAPI).
Without CCE: Claude reads payments.py + shipping.py = 45,000 tokens
With CCE: context_search "payment flow" = 800 tokens
We benchmarked CCE against FastAPI (53 source files, 180K tokens) with 20 real coding questions. No cherry-picking, no synthetic queries.
Methodology: For each query, "without CCE" means reading the full content of every file the query touches. "With CCE" means the relevant chunks after compression.
Important baseline note: The 94% number is measured against full-file reads, not against what Claude Code actually does. In practice, Claude Code already uses grep, partial file reads, and targeted tools, so the real-world savings compared to normal Claude Code behavior will be lower than 94%. We use full-file as the baseline because it's reproducible and deterministic (no agent behavior variability). The benchmark measures CCE's retrieval efficiency, not a head-to-head comparison with Claude Code's built-in exploration.
Output compression (reducing Claude's reply length) provides additional savings (~65% estimated) but is not included in the headline number above.
Django (2,347 files, 5.4M tokens) shows CCE scales to large codebases with 0.95 recall. Go's shorter files reduce the retrieval headroom (smaller baseline). Monorepos dilute recall at top-10 (fiber). Middleware queries with one-feature-per-file hit R=1.00 consistently.
Reproduce it yourself:
pip install code-context-engine
python benchmarks/run_benchmark.py --repo https://github.com/fastapi/fastapi.git --source-dir fastapi
python benchmarks/run_benchmark.py --repo https://github.com/go-chi/chi.git --source-dir .
Full results in benchmarks/results/. Queries and methodology in benchmarks/.
11 MCP tools that Claude uses automatically:
Live dashboard with donut charts, file health, and session history:
cce dashboard
Dollar estimates with multi-provider pricing (Anthropic, OpenAI, Google):
cce savings --all # see savings across all projects
context_search. Hybrid vector + BM25 retrieval finds the right chunks. Code graph adds related files automatically.session_recall.cce savings shows exactly how much you saved.Re-indexing after edits takes under 1 second (96% embedding cache hit rate). Git hooks keep the index current automatically.
Output compression tools (like Caveman) save 20-75% on output tokens. Output is 5-15% of your bill. Net savings: ~11%.
CCE saves on input tokens (94% retrieval savings on FastAPI, reproducibly benchmarked). Input is 85-95% of your bill.
Not a text search. Tree-sitter AST parsing creates semantic chunks. Hybrid retrieval merges vector similarity with BM25 keyword matching via Reciprocal Rank Fusion. A confidence scorer blends similarity (50%), keyword match (30%), and recency (20%). Graph expansion walks CALLS/IMPORTS edges to pull in related code.
record_decision("use JWT for auth", reason="session tokens flagged by legal") is stored in SQLite and surfaces via session_recall in the next session. No re-explaining your architecture.
Not estimates. Actual tokens served vs full-file baseline, broken down by buckets (retrieval, compression, output, memory, grammar). Dollar costs fetched from Anthropic's pricing page. Savings summary shown at every session start.
Secret files (.env, *.pem, credentials.json) are never indexed. Content is scanned for AWS keys, GitHub tokens, Slack tokens, Stripe keys, JWTs, and generic credentials. PII (emails, IPs, SSNs, credit cards) is scrubbed from memory writes. All MCP file paths are validated against path traversal.
SHA-256 fingerprint per chunk, salted with model name. Re-index skips unchanged code. Binary float32 storage (10x smaller than JSON). Typical re-index: 96% cache hit, under 1 second.
Replaced LanceDB with sqlite-vec. Same cosine-distance quality, 99% smaller install. WAL mode + PRAGMA NORMAL for 80% write speedup. Vectors, FTS5, code graph, and compression cache all in three SQLite files.
Memory entries compressed without LLM calls. Drops articles, fillers, pronouns. Three levels (lite/full/ultra, 20-60% savings). Code, paths, URLs preserved byte-for-byte. Same input always yields same output.
5 Claude Code lifecycle hooks capture session context. Every hook runs curl ... || true, so a crashed server never blocks the user. SessionStart injects bootstrap context; others capture silently.
Dollar estimates in cce savings support 15+ models across Anthropic, OpenAI, and Google. Static pricing ships with CCE, live Anthropic pricing is fetched and cached 7 days. Configure pricing.model (e.g. gpt-4o, gemini-2.5-pro, sonnet) or override with pricing.input / pricing.output for custom rates.
Running dozens of cce serve processes (one per project per AI session) can exhaust system memory. The resource governor caps ONNX Runtime threads per process, uses advisory file locks so only one process indexes a given project at a time, backs off under Linux memory pressure (PSI), and auto-shuts down idle servers after 30 minutes. Configure via serve.idle_timeout_minutes and serve.max_ort_threads.
CCE's cross-session memory depends on the agent calling record_decision and record_code_area. Memory nudges make recording ambient: after N searches without a recording, context_search results include a short reminder. At session end, the Stop hook summarizes unrecorded activity. Nudges re-arm after the first recording so they stay useful without being noisy.
cce serve --http exposes a POST /search endpoint for custom agent integrations that speak HTTP instead of MCP stdio. Same hybrid retrieval pipeline, structured JSON response with confidence scores. Input validation clamps top_k (1..100) and confidence_threshold (0.0..1.0).
7 buckets track every token saved: retrieval, chunk compression, output compression, memory recall, grammar, turn summarization, progressive disclosure. Survives restarts. Powers CLI and dashboard analytics.
Agent Plugins is an open standard (v1.0.0) backed by Amazon, Cursor, Microsoft, OpenAI, and Vercel for packaging AI skills and MCP servers into portable, zero-install bundles. CCE can generate a plugin directory that compatible editors can discover and load automatically.
cce init --plugin # Generate at .cce/plugin/
cce init --plugin --plugin-dir ~/plugins/cce # Custom location
cce init --agent claude --plugin # Both: agent config + plugin
.cce/plugin/
├── plugin.json # Agent Plugins v1.0.0 manifest
├── mcp.json # MCP server config (uvx + stdio)
├── skills/
│ └── code-context/
│ ├── SKILL.md # Agent instructions (frontmatter + body)
│ └── references/
│ └── tools.md # Per-tool parameter docs (loaded on demand)
└── LICENSE
VS Code, GitHub Copilot, ChatGPT, Codex, Cursor, and Kiro. The plugin uses uvx to launch CCE on demand, so users do not need to pre-install the Python package. The MCP server auto-discovers the project root by walking up from its working directory, looking for .context-engine.yaml or .git/.
--plugin vs --agentBoth can be used together. --agent handles per-editor MCP config, --plugin provides a portable alternative.
cce init # Index + install hooks + register MCP
cce init --plugin # Generate Agent Plugin for VS Code, Cursor, etc.
cce # Status banner
cce savings # Token savings with dollar estimates
cce savings --all # All projects
cce dashboard # Web dashboard with live charts
cce search "auth flow" # Test a query
cce status # Index health + config
cce services # Ollama + dashboard + MCP status
cce commands add-rule '...' # Project rules for Claude
cce uninstall # Clean removal of all CCE artifacts
Run cce list for the full command reference.
Zero-config by default. Override what you need in ~/.cce/config.yaml or .context-engine.yaml:
compression:
level: standard # minimal | standard | full
output: standard # off | lite | standard | max
ollama_url: http://localhost:11434 # point at a remote Ollama if desired
retrieval:
top_k: 20
confidence_threshold: 0.5
pricing:
model: opus # opus | sonnet | haiku | gpt-4o | gemini-2.5-pro | ...
# input: 15.0 # override $/1M input tokens
# output: 75.0 # override $/1M output tokens
Remote Ollama: If you run Ollama on another machine in your network, set compression.ollama_url (e.g. http://nas.local:11434) or export CCE_OLLAMA_URL (the env var wins). CCE probes the endpoint and falls back to truncation-only compression when it's unreachable, so a flaky link won't break indexing.
CCE also compresses Claude's responses (same concept as Caveman):
Tell Claude: "switch to max compression" or "turn off compression". Code blocks and commands are never compressed.
No GPU required. With Ollama, embeddings are handled by the Ollama server. With the [local] extra, the embedding model runs on CPU via ONNX Runtime.
AST-aware chunking (tree-sitter parsed, 11 extensions):
Language-aware fallback chunking (40+ extensions):
All other text files are chunked by line range. Binary files are skipped.
No. Quality stays the same or slightly improves.
CCE replaces "dump the entire file" with "search for the relevant function." The model still gets the code it needs (0.90 Recall@10 in benchmarks). Less irrelevant context means less noise competing for attention, which can improve the model's focus on your actual question.
CCE writes output compression rules directly into your agent's instruction files (CLAUDE.md, AGENTS.md, .cursorrules, etc.) during cce init. These rules apply to the entire session, not just CCE tool responses, so every reply from the agent follows them.
Set the level in ~/.cce/config.yaml or .context-engine.yaml:
compression:
output: max # off | lite | standard | max
Then re-run cce init to update instruction files. Or change at runtime:
set_output_level output_level=max
Default is standard. All levels include code output rules that tell the model to show only changed lines (not full file rewrites), which is where most output tokens go in coding sessions. The max level produces very terse prose (similar to "caveman mode"). Code blocks, paths, and commands are never compressed regardless of level.
Most savings are input tokens (what goes into the model):
Output tokens cost 5x more per token (e.g. Opus: $15/1M input vs $75/1M output), so even a small output reduction has outsized cost impact.
See CHANGELOG.md for shipped features.
Contributions welcome. See https://github.com/elara-labs/code-context-engine/blob/main/CONTRIBUTING.md for setup.
MIT. See LICENSE.
Claude Code · MCP · sqlite-vec · Tree-sitter · fastembed · Ollama
Index your codebase. AI searches instead of re-reading files. 94% token savings.
{ "mcpServers": { "io.github.ai-elara-code-context-engine": { "command": "uvx", "args": [ "code-context-engine==0.4.13" ] } }}ツール
ツール一覧はまだありません。リモートサーバーは、最初のヘルスチェック後に取得されます。
よくある質問
このページで Cursor、Claude Desktop、Claude Code、VS Code 用の stdio 設定をコピーできます。PlugMCP のプロキシではなく、公開元のインストール方法を使います。
このレコードに必須の環境変数は記載されていません。
Code Context Engine は stdio 経由でローカル実行されます。
最終チェックは 2026-11-09 です。 Index your codebase. AI searches instead of re-reading files. 94% token savings.