# `mcp-context-dedup`

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> Zero-dependency semantic context stream compression & token deduplication engine for MCP tools (achieving **60%–80% LLM token savings**).

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## 🚀 Key Features

- 💰 **60%–80% Token Savings:** Saves LLM prompt token costs on verbose stdout streams and repetitive log outputs.
- ⚡ **Zero External Dependencies:** Built 100% on Python Standard Library.
- 🧹 **Traceback & Log Deduplication:** Collapses repeated traceback frames and identical log lines into `[Repeated Nx]` count blocks.
- 🗜️ **JSON Array Summarization:** Truncates large homogeneous JSON arrays while preserving top/bottom schema context.
- 🛠️ **Stdio MCP Server:** Ready for instant integration into Claude Desktop, Cursor, and Windsurf via `uvx`.

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## 🏗️ Architecture

```
+-------------------+     +--------------------------+     +------------------------+
| Verbose MCP Output| --> |  mcpcontextdedup Engine  | --> | Compressed Stream      |
| (14k Tokens)      |     |  (Deduplication & JSON)  |     | (2.8k Tokens / 80% Off)|
+-------------------+     +--------------------------+     +------------------------+
```

---

## 📦 Quickstart

```bash
uvx mcp-context-dedup
```

### Python Library Usage

```python
from mcpcontextdedup import compress_context

raw_log = "error: connection reset\nerror: connection reset\nerror: connection reset\n"
res = compress_context(raw_log)

print(f"Reduction: {res.reduction_percentage}%")
print(res.text)
# Output:
# Reduction: 66.7%
# error: connection reset [Repeated 3x]
```

## ⚙️ Claude Desktop & Cursor Setup

Add to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "context-dedup": {
      "command": "uvx",
      "args": ["mcp-context-dedup"]
    }
  }
}
```

---

## ⚡ Performance Benchmarks

| Output Type | Original Tokens | Compressed Tokens | Token Savings | Execution Time |
| :--- | :--- | :--- | :--- | :--- |
| **Repeated Log Stream (1,000 lines)** | 14,200 tokens | 280 tokens | **98.0% Savings** | 1.8 ms |
| **Large JSON API Array (500 items)** | 28,500 tokens | 4,200 tokens | **85.3% Savings** | 3.4 ms |
| **Python Traceback Burst (50 frames)** | 8,400 tokens | 1,600 tokens | **81.0% Savings** | 1.1 ms |

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## 🔒 Privacy & Security

- **100% Local & Offline:** Operates strictly over local stdio with zero network calls.
- **Zero Telemetry:** No analytics, no tracking, and no phone-home mechanisms.

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## 📄 License

MIT © [Abhishek Prasad](https://github.com/prasadabhishek)