Optimize AI workflows with powerful TypeScript summarization functions for command, file, directory, and text content
The Intelligent Text Summarization Server provides advanced summarization capabilities to augment the performance and reliability of AI applications, particularly within the context of Model Context Protocol (MCP). This MCP server offers a clean, extensible architecture built with modern TypeScript that integrates seamlessly with various AI workflows. It focuses on optimizing the use of context windows in AI agents like Roo Cline and Cline by providing concise summaries of large outputs.
The Intelligent Text Summarization Server introduces several key features tailored to enhance AI applications:
The server is designed to be a versatile component of the Model Context Protocol ecosystem, enabling seamless integration with various MCP clients such as Claude Desktop and Continue. It leverages intelligent content cache management and summarization functions to ensure optimal performance by reducing unnecessary data overflows in AI agent contexts.
graph TD
A[AI Application] -->|MCP Client| B[MCP Server]
B --> C[Data Source/Tool]
style A fill:#e1f5fe
style C fill:#f3e5f5
This diagram illustrates the flow of data and commands from an AI application through its MCP client, to the summarization server, and eventually to a relevant data source or tool.
Getting started is straightforward using npm. To install the Intelligent Text Summarization Server:
npm i mcp-summarization-functions
This command will install the necessary package into your project dependencies.
Imagine a scenario where an AI agent needs to process large amounts of data from command executions. By employing the summarize_command function, this server can efficiently manage context windows and maintain the quality of outputs. For example:
{
"command": "ls -l /var/log",
"cwd": "/opt"
}
During a security audit, summarizing directory structures with summarize_directory can provide a quick overview without flooding the context window. This example focuses on understanding potential vulnerabilities:
{
"path": "./app",
"cwd": "/opt/project",
"recursive": true,
"hint": "security_analysis"
}
The Intelligent Text Summarization Server supports multiple AI clients, including Claude Desktop and Continue, as indicated in the following compatibility matrix:
| MCP Client | Resources | Tools | Prompts | Status |
|---|---|---|---|---|
| Claude Desktop | ✅ | ✅ | ✅ | Full Support |
| Continue | ✅ | ✅ | ❌ | Tools Only |
This matrix highlights the level of support for different MCP clients, ensuring compatibility and seamless integration.
The server's configuration can be customized to work with different AI providers via environment variables. Here is an example configuration snippet:
PROVIDER=ANTHROPIC
API_KEY=your-anthropic-key
MODEL_ID=claude-3-5-sonnet-20241022
This setup ensures that the server can adapt to various AI models and providers, enhancing flexibility.
Advanced configuration options allow for fine-tuned control over summarization processes. These include:
MAX_TOKENS: Maximum tokens allowed in model responses.SUMMARIZATION_CHAR_THRESHOLD: Threshold at which summation is triggered.SUMMARIZATION_CACHE_MAX_AGE: Cache duration to improve performance.PROVIDER=ANTHROPIC
API_KEY=your-anthropic-key
MODEL_ID=claude-3-5-sonnet-20241022
MCP_WORKING_DIR=default_working_directory
A: The summarize_command function executes a given command and provides a concise summary of its output, ensuring context window optimization.
A: Yes, you can set the MODEL_ID environment variable to choose a different model for specific tasks or purposes like "security_analysis."
A: Use the GOOGLE provider and specify the appropriate API key. Ensure the MCP_WORKING_DIR is correctly configured.
A: Context overflow can lead to performance degradation and potential failure of AI agents relying on specific context to function accurately.
A: Yes, this server supports multiple providers including OpenAI-compatible APIs that can be configured for continual learning tasks.
Contributions are welcome! To contribute, please follow these guidelines:
npm run test to ensure existing tests pass with your changes.The Intelligent Text Summarization Server is part of the broader MCP ecosystem, designed to facilitate advanced AI application development and integration. Explore additional resources and community documentation at MCP Official Website.
This comprehensive documentation highlights the robust features and capabilities of the Intelligent Text Summarization Server for Model Context Protocol applications, offering detailed insights into its implementation and usage in various real-world scenarios.
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