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mcp-summary/README.md
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# MCP Summary Server
An MCP (Model Context Protocol) server for document summarization that keeps full text out of the chat context window.
## Features
- Automatically determines whether to summarize directly or use chunked summarization
- All processing happens server-side
- Returns only the summary to the client
- Configurable chunking parameters
- Bearer token authentication (optional)
## Setup
### Environment Variables
Copy `.env.example` to `.env` and configure:
```bash
cp .env.example .env
```
| Variable | Default | Description |
|----------|---------|-------------|
| PORT | 8080 | HTTP server port |
| API_KEY | (empty) | Bearer token for authentication |
| OPENAPI_URL | http://localhost:8080/v1 | LLM API endpoint |
| OPENAPI_API_KEY | (empty) | LLM API key |
| MODEL_NAME | gpt-4o | LLM model to use |
| CHUNK_SIZE | 4000 | Characters per chunk |
| OVERLAP | 200 | Characters of overlap between chunks |
| TARGET_INTERMEDIATE_SUMMARY_LENGTH | 150 | Words per chunk summary |
| MAX_DIRECT_SUMMARY_LENGTH | 100 | Max final summary length |
| MAX_DIRECT_TEXT_LENGTH | 8000 | Max text length before chunking |
## Running
### Docker
```bash
# Build
docker build -t mcp-summary .
# Run with environment file
docker run -p 8080:8080 --env-file .env mcp-summary
# Run with inline environment variables
docker run -p 8080:8080 \
-e OPENAPI_URL=http://localhost:8080/v1 \
-e OPENAPI_API_KEY=your-key \
-e MODEL_NAME=gpt-4o \
mcp-summary
```
### Python
```bash
pip install -r requirements.txt
python mcp_summary_server.py
```
## MCP Tool
### summarize_document
Summarizes a document, automatically handling chunking for long text.
**Parameters:**
- `text` (string, required): The document text to summarize
- `max_length` (integer, optional): Maximum summary length in words (default: 100)
**Returns:**
```json
{
"summary": "The summarized text...",
"original_length": 12345,
"method": "direct", // or "chunked"
"chunks": 1 // number of chunks used
}
```