devutils-mcp-server

Every developer needs to hash strings, encode/decode data, generate UUIDs, decode JWTs, format JSON, calculate CIDR ranges, and convert timestampsevery day. DevUtils MCP Server brings all of these tools directly into your AI assistant — works with Claude, Cursor, VS Code, Windsurf, and any other MCP-compatible client.

Use theAdd to Cursor/VS Codebadges above, or install the plugin:

/plugin marketplace add paladini/devutils-cursor-plugin /plugin install devutils-mcp@devutils-cursor-plugin

Cursor:Settings → Customize, or add from GitHubpaladini/devutils-cursor-plugin.

npm install -g devutils-mcp-server devutils-mcp-server

# Published image (when available) docker run -i --rm ghcr.io/paladini/devutils-mcp-server # Or build locally docker build -t devutils-mcp-server . docker run -i --rm devutils-mcp-server # Or with Compose docker compose build docker compose run --rm -i devutils-mcp

# After authenticating to npm.pkg.github.com for the @paladini scope: npx -y @paladini/devutils-mcp-server

One-click:use theAdd to Cursorbadge at the top of this README.

src/ ├── index.ts # MCP server entry point (stdio transport) └── tools/ ├── hash.ts # Cryptographic hash functions ├── encoding.ts # Encode/decode utilities ├── generators.ts # ID and password generators ├── jwt.ts # JWT decode and validation ├── formatters.ts # JSON formatting and querying ├── converters.ts # Data type and unit converters ├── network.ts # Network calculation utilities └── text.ts # Text analysis and manipulation

Zero external API dependencies.All tools run locally with no network calls.

The image uses a multi-stage build for minimal size:
- Build stage:Compiles TypeScript on Node 22 Alpine
- Runtime stage:Runs compiled JS on Node 22 Alpine as a non-root user

docker build -t devutils-mcp-server . # Smoke-test with an MCP initialize request echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}' | docker run -i --rm devutils-mcp-server

Valid criticism:If you're writing Python scripts and need to hash something,hashlibis 2 lines of code. Why run MCP overhead?

Answer:This server is optimized forAI agentsin multi-step workflows, not programmers writing code:
- AI hallucination cost >> MCP overhead— An AI model spending 50ms calling an MCP tool (vs. 1ms library call) is negligible when the alternative is the modelmaking up a hashor using the wrong encoding.
- Reliable tool semantics— MCP enforces strict tool contracts. For example,jwt_decodealwaysreturns human-readable dates with timezone support.
- Universally accessible— Any MCP-compatible client can use these tools. A Python library only works if your agent is Python-based.
- Multi-tenant safety— MCP provides explicit tool whitelisting with input validation.

When to use DevUtils versus alternatives

- You're using Claude, Cursor, VS Code Copilot, Windsurf, or any MCP-compatible AI assistant
- You want reliable, validated utility operations in AI workflows
- You need 36 tools in one package

- You're writing regular application code (use native libraries)
- You need extreme performance (direct library calls are faster)
- Your AI client does not support MCP

- Small & focused:36 utilities, zero external APIs, ~50MB container
- Security-first:Non-root user, Alpine Linux, minimal attack surface
- AI-friendly:Consistent naming (<domain>_<operation>), strict schemas, human-readable outputs
- Client-agnostic:Works with any MCP-compatible client via stdio transport
- Fork the repository
- Create your feature branch (git checkout -b feat/amazing-tool)
- Commit your changes (git commit -m 'feat: add amazing tool')
- Push to the branch (git push origin feat/amazing-tool)
- Open a Pull Request

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