Statelessor MCP Server
# Statelessor MCP Server
MCP (Model Context Protocol) server for analyzing .NET and Java projects for stateful code patterns. Integrates with Amazon Q Developer.
## Installation
```bash
npm install -g statelessor-mcp
```
## Quick Start
### 1. Configure Amazon Q
Create or edit `~/.aws/amazonq/mcp-config.json`:
```json
{
"mcpServers": {
"statelessor": {
"command": "npx",
"args": ["statelessor-mcp"],
"env": {
"STATELESSOR_API_URL": "https://statelessor-api.port2aws.pro"
}
}
}
}
```
### 2. Restart Amazon Q in your IDE
### 3. Use in Amazon Q Chat
```
You: Analyze my local project at /path/to/my-dotnet-app
You: Analyze https://github.com/myorg/java-project
You: Explain how to fix Session State issues
You: Generate a bash script for analyzing .NET projects
```
## Available Tools
- **analyze_git_repository** - Analyze Git repos for stateful patterns
- **analyze_local_project** - Analyze local project directories
- **generate_analysis_script** - Generate bash/PowerShell scripts
- **get_project_findings** - Retrieve historical findings
- **explain_remediation** - Get remediation guidance
## Configuration
Environment variables:
- `STATELESSOR_API_URL` - API endpoint (default: http://localhost:3001)
- `STATELESSOR_API_TIMEOUT` - Request timeout in ms (default: 300000)
## Additional details
Read USER_GUIDE.md for more details
Read INTEGRATION.md as well for additional information
## License
MITTDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: analyze_git_repository and analyze_local_project target different source locations, explain_remediation provides guidance, generate_analysis_script creates scripts, and get_project_findings retrieves historical data. No overlap or ambiguity exists between these functions.
All tools follow a consistent verb_noun pattern with snake_case naming (e.g., analyze_git_repository, explain_remediation, generate_analysis_script). The naming is predictable and readable throughout the set.
With 5 tools, the server is well-scoped for analyzing stateful code patterns. Each tool serves a unique and necessary function in the workflow, from analysis to remediation and historical retrieval, without being too sparse or bloated.
The toolset covers core analysis workflows (git/local analysis, remediation guidance, script generation, and findings retrieval). A minor gap exists in direct remediation actions (e.g., fixing patterns automatically), but agents can work around this using the provided guidance and scripts.