About t-rush-mcp
t-rush-mcp exposes t-rush technical-debt scanning and streak tracking to AI coding agents. It can find and search TODO comments, verify their removal, and synchronize streak updates with the local t-rush database.
Key features
- 01Find and rank TODO and FIXME comments
- 02Fuzzy-search comments
- 03Verify resolved comments
- 04Scan raw repository debt
- 05View streak status and stats
- 06Aggregate debt across repositories
Use cases
- 01Find old TODOs for an coding agent to address
- 02Search comments for a specific task
- 03Verify a TODO was removed and update a streak
- 04Review technical debt across local repositories
MCP support
Exposes t-rush scanning, search, resolution, streak, and debt aggregation functions as MCP tools for AI coding agents.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
6Saved tool list · updated 3w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| count | number | No | Number of top items to return (default 1) |
| repo_path | string | Yes | Absolute path to the repository |
Details
From the project README
t-rush-mcp
Speedrun your technical debt, now powered by AI.
An MCP (Model Context Protocol) server that exposes the power of t-rush directly to AI coding assistants like Antigravity, Claude, and others. Let your AI agents find, prioritize, and fix TODO · FIXME · BUG comments across your codebase.
Why t-rush-mcp?
AI coding agents are great at fixing bugs, but they often struggle to know what to work on when left unattended. t-rush-mcp bridges this gap by exposing your codebase's technical debt directly to the AI through standard MCP tools.
Agents can now query your project for open TODOs, filter them by keyword, prioritize the oldest debt, and even increment your personal t-rush completion streak when they successfully resolve them.
💻 Want to fix TODOs manually?
Try the t-rush CLI to speedrun your own TODOs! Pick a bug, start a timer, fix the code, and build a massive completion streak yourself.
Features
find_todos: Feeds the agent all open TODO/FIXME comments so it can rank them by age and severity natively.search_todos: Allows the agent to fuzzy-search existing comments to find specific tasks.resolve_todo: Verifies the comment has been removed from the file and automatically increments the t-rush streak.scan_todos_raw: Returns an unranked, raw list of all tech debt in a repository.get_streak_status: Exposes the user's current streak and stats to the agent.aggregate_debt: Summarizes the total debt across multiple local repositories.
Install & Configuration
1. Via npm (Global Install)
You can install the MCP server globally on your machine using npm:
npm install -g @devds1989/t-rush-mcp
2. Manual Configuration
To add this server manually to your client's configuration file (e.g. claude_desktop_config.json):
{
"mcpServers": {
"t-rush": {
"command": "npx",
"args": ["-y", "@devds1989/t-rush-mcp"],
"eager": true
}
}
}
Or test it directly via npx:
npx -y @devds1989/t-rush-mcp
3. Claude Code
To add this server directly to Anthropic's Claude Code CLI:
claude mcp add t-rush -- npx -y @devds1989/t-rush-mcp
Requirements: Node.js 18+
How it works
t-rush-mcp uses the shared @devds1989/trush-core logic to scan your codebase using the exact same parsers as the t-rush CLI. All streak updates and history modifications made by the AI agent are instantly synchronized with your local ~/.t-rush/data.json database.
When an AI agent resolves a TODO, it increments your streak—teamwork!
Supported languages
t-rush detects TODO and FIXME in all common comment styles:
| Style | Languages |
|---|---|
// | JavaScript, TypeScript, Go, Rust, C, C++, Java, Kotlin, Swift, Dart |
# | Python, Ruby, Shell, YAML, R, Perl, Elixir, Crystal |
-- | SQL, Lua, Haskell, Ada |
% | Erlang, LaTeX |
; | Lisp, Clojure, Assembly |
* | Inside /* */ block comments |
Contributing
Contributions are welcome. Please open an issue before submitting a large PR.
git clone https://github.com/DevDs1989/trush-mcp
cd trush-mcp
npm install
npm run build
License
MIT © Dev
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.