Tsap MCP Server

The TSAP MCP Server is a text search and analysis processing system based on the Model Context Protocol (MCP), providing standardized interface services for code intelligence and text analysis. The project consists of three major components: core TSAP functions, tool APIs, and the MCP adaptation layer. It supports various functions such as text search, code analysis, and data processing, and can be seamlessly integrated with MCP clients such as Claude Desktop.

Developer tools Search tools #Text Analysis #Code Search #MCP Protocol #AI Tools .Python

rating : 2.5 points

downloads : 18

update time : 2025-04-29

What is the TSAP MCP Server?

The TSAP MCP Server is a standardized interface server that exposes powerful text search, code analysis, and document processing functions to AI assistants (such as Claude Desktop) through the MCP protocol. It enables AI to understand code, analyze document content, and perform complex text processing tasks.

How to use the TSAP MCP Server?

After installation, the server will automatically integrate with compatible AI assistants. You can directly interact with the AI through natural language, and the AI will call various tools on the server to complete your requests.

Use cases

Scenarios that require in - depth text processing capabilities, such as code search and analysis, document content extraction, text data processing, project structure exploration, and code quality inspection.

Main Features

Intelligent SearchSupports text, code, and semantic search, and can filter results by multiple conditions such as file type and path.

Text AnalysisProvides advanced analysis functions such as document structure analysis, code quality inspection, and content feature extraction.

Text ProcessingIncludes various processing tools such as text standardization, pattern extraction, and data conversion.

Data VisualizationSupports intuitive display of analysis results in the form of charts, network diagrams, etc.

Advantages and Limitations

Advantages

Standardized interface, compatible with multiple AI assistants

Powerful text processing and analysis capabilities

Flexible search and filtering options

Scalable architecture design

Limitations

Requires Python environment support

Some advanced functions require additional dependencies

Performance optimization may be required when processing large - scale documents

How to Use

Install the Server

Ensure that Python 3.13+ and ripgrep are installed on the system, and then run the installation command.

Start the Server

Use the command - line tool to start the MCP server.

Integrate into AI Assistant

In compatible AI assistants such as Claude Desktop, the server will be automatically discovered and used.

Usage Examples

Code SearchFind all code snippets using a specific API in the project.

Document AnalysisExtract the key parameter tables from technical documents.

Code Quality InspectionCheck potential problems in Python files.

Frequently Asked Questions

Which AI assistants does the TSAP MCP Server support?

How to handle searches in large codebases?

Is an internet connection required for use?

How to extend the server functions?

Related Resources

MCP Protocol Documentation

The official specification document of the Model Context Protocol.

GitHub Repository

Project source code and issue tracking.

Usage Tutorial Video

An introductory video tutorial for the TSAP MCP Server.

🚀 TSAP MCP Server

The TSAP MCP Server is a core component based on the MCP protocol, designed for text analysis and processing tasks. It provides a unified interface to support multiple functions including file access, project management, configuration management, and semantic data retrieval.

🚀 Quick Start

Starting the Server

Basic Startup:
```
tsap-mcp run
```
Development Mode (Auto Reload):
```
tsap-mcp run --reload
```
Test Compatibility:
```
tsap-mcp test --compatibility
```

Install to System or Desktop Environment:

tsap-mcp install [--system] [--desktop]

✨ Features

The TSAP MCP Server supports various functions such as file access, project management, configuration management, and semantic data retrieval through a unified interface.

📦 Installation

Installation Steps

Clone the repository:

git clone https://github.com/your-org/tsap-mcp.git
cd tsap-mcp

Install in development mode (optional development dependencies):
```
pip install -e ".[dev]"
```
Run tests:
```
pytest
```

💻 Usage Examples

Server Configuration

The server's behavior can be configured via the following environment variables:

Property	Details
`TSAP_HOST`	Bound host address (Default: 127.0.0.1)
`TSAP_PORT`	Bound port (Default: 8000)
`TSAP_LOG_LEVEL`	Log level (Default: INFO)
`TSAP_PERFORMANCE_MODE`	Performance mode (balanced, speed, accuracy)
`TSAP_CACHE_ENABLED`	Enable cache (Default: true)
`TSAP_DEBUG`	Enable debug mode (Default: false)

📚 Documentation

Server Architecture

Module Structure

src/
├── tsap/                  # Core TSAP implementation
│   ├── __init__.py        # Package initialization
│   ├── server.py          # Main server implementation
│   ├── cli.py             # Command-line interface
│   ├── config.py          # Configuration management
│   ├── main.py            # Entry point
│   ├── api/               # API layer 
│   ├── analysis/          # Text and code analysis
│   ├── core/              # Core functions
│   ├── project/           # Project management
│   ├── storage/           # Storage tools
│   ├── utils/             # Utility functions
│   ├── toolapi/           # Tool API protocol implementation
│   │   ├── __init__.py    # Package initialization
│   │   ├── server.py      # Server implementation
│   │   ├── client.py      # Client implementation
│   │   └── protocol.py    # Protocol definition
│   └── mcp/              # MCP protocol implementation
├── tsap-mcp/             # MCP server main directory
│   ├── __init__.py       # Package initialization
│   ├── server.py          # Server implementation
│   ├── cli.py             # Command-line interface
│   └── config.py          # Configuration management
└── tests/                # Test cases
   └── test_*.py           # Test cases for each component

Data Flow

Request Handling:
- The client sends a request to the server.
- The server parses and validates the request.
- Calls the corresponding processing logic based on the functional module.

Core Process:

Client → Parser → Validator → Processor → Response Generator → Client

Component Interaction:
- toolapi: Responsible for communication between tools.
- mcp: Implements the MCP protocol interface.
- storage: Manages data storage.

Development Environment

Project Initialization

Clone the repository:

git clone https://github.com/your-org/tsap-mcp.git
cd tsap-mcp

Install dependencies:
```
pip install -r requirements.txt
```
Start the development server:
```
tsap-mcp run --reload
```

Contribution Guidelines

Code Submission Specification:
- Follow the PEP8 code style.
- Provide detailed commit messages.
Issue Reporting:
- Submit issues to the GitHub repository: https://github.com/your-org/tsap-mcp/issues
Contribute Code:
- Fork the project.
- Create a feature branch.
- Submit a Pull Request.

Architectural Design

Module Division

toolapi: Responsible for implementing the communication protocol between tools.
mcp: Implements the core MCP protocol interface.
storage: Provides data storage and retrieval functions.
project: Manages project-related configurations and operations.

Data Flow Design

Request Handling Process:

Client → Parser → Validator → Processor → Response Generator → Client

Core Component Interaction:
- toolapi is responsible for communication between tools.
- mcp implements the MCP protocol interface.
- storage manages data storage.