Wise Mise MCP

Wise Mise MCP

AI-powered MCP server that analyzes project structures to create optimized task architectures for mise, enabling intelligent task management, dependency mapping, and automated task suggestions.

Category
Visit Server

README

🎯 Wise Mise MCP

The intelligent MCP server that transforms mise task management with AI-powered analysis and domain expertise

PyPI version Python 3.9+ License: MIT Code style: black Documentation Deploy to Smithery

Stop wrestling with mise configuration. Wise Mise MCP brings enterprise-grade intelligence to your mise workflow, automatically analyzing your project structure and creating perfectly organized, maintainable task architectures that scale with your development needs.

Why Wise Mise MCP?

🧠 Intelligent Task Analysis

  • Automatically analyzes your project structure to extract meaningful tasks
  • Understands 10+ technology domains (build, test, lint, deploy, CI/CD, etc.)
  • Suggests optimal task organization and dependency patterns

πŸ—οΈ Architecture-Aware

  • Follows mise best practices with hierarchical task organization
  • Supports complex dependency graphs with source/output tracking
  • Optimizes for incremental builds and performance

πŸ”§ Developer Experience

  • Integrates seamlessly with any MCP-compatible client
  • Provides detailed explanations and recommendations
  • Reduces cognitive load of task management

Quick Start

Using UVX (Recommended)

# Just run this to start Wise Mise MCP with UVX
uvx wise-mise-mcp

# Or install globally
uv tool install wise-mise-mcp

Traditional pip

pip install wise-mise-mcp

Add to Your MCP Client

Add to your MCP client configuration (e.g., Claude Desktop):

{
  "mcpServers": {
    "wise-mise-mcp": {
      "command": "uvx",
      "args": ["wise_mise_mcp"]
    }
  }
}

Deploy with Smithery (One-Click Deployment)

Deploy to Smithery

Deploy Wise Mise MCP instantly to Smithery's cloud infrastructure with one click. Smithery provides managed MCP server hosting with built-in monitoring, scaling, and zero-configuration deployment.

Quick Smithery Deployment:

  1. Click the "Deploy to Smithery" badge above
  2. Connect your GitHub account (if needed)
  3. Your MCP server will be automatically deployed and configured
  4. Use the provided endpoint in your MCP client configuration

Benefits of Smithery Deployment:

  • βœ… Zero server management overhead
  • βœ… Automatic scaling and load balancing
  • βœ… Built-in monitoring and health checks
  • βœ… Global CDN for low-latency access
  • βœ… Automatic SSL/TLS encryption
  • βœ… Integration with popular MCP clients

What Makes It "Wise"?

Wise Mise MCP goes beyond simple task creation. It brings intelligence to your mise configuration:

πŸ” Project Analysis

# Analyzes your entire project structure
analyze_project_for_tasks("/path/to/project")
# Returns strategically organized tasks based on your tech stack

πŸ•ΈοΈ Dependency Mapping

# Traces complex task relationships
trace_task_chain("/path/to/project", "build:prod")
# Visualizes the complete execution flow

⚑ Smart Task Creation

# Intelligently places tasks in the right domain
create_task(
    project_path="/path/to/project",
    task_description="Run TypeScript type checking",
    # Automatically suggests: lint:types with proper dependencies
)

Core Features

🎯 Domain Experts

  • Build: Frontend/Backend build systems, bundlers, compilers
  • Test: Unit, integration, e2e testing strategies
  • Lint: Code quality, formatting, static analysis
  • Deploy: CI/CD, containerization, release management
  • Database: Migrations, seeding, schema management
  • Development: Local dev servers, hot reloading, debugging

πŸ“Š Intelligent Analysis

  • Complexity Assessment: Automatically categorizes tasks as Simple, Moderate, or Complex
  • Dependency Detection: Identifies natural task relationships
  • Source/Output Tracking: Optimizes incremental builds
  • Redundancy Elimination: Finds and removes duplicate tasks

πŸ”§ MCP Tools

Tool Purpose
analyze_project_for_tasks Extract strategic tasks from project structure
trace_task_chain Map task dependencies and execution flow
create_task Add new tasks with intelligent placement
prune_tasks Remove outdated or redundant tasks
validate_task_architecture Ensure configuration follows best practices
get_task_recommendations Get suggestions for optimization

Example Workflows

Analyzing a New Project

# Let Wise Mise MCP analyze your project
> analyze_project_for_tasks("./my-app")

βœ… Detected: Next.js + TypeScript + Prisma
πŸ“‹ Suggested Tasks:
  β”œβ”€β”€ build:dev (next dev)
  β”œβ”€β”€ build:prod (next build)
  β”œβ”€β”€ test:unit (jest)
  β”œβ”€β”€ test:e2e (playwright)
  β”œβ”€β”€ lint:code (eslint)
  β”œβ”€β”€ lint:types (tsc --noEmit)
  β”œβ”€β”€ db:migrate (prisma migrate)
  └── deploy:vercel (vercel deploy)

Understanding Task Dependencies

# Trace the execution flow
> trace_task_chain("./my-app", "deploy:prod")

πŸ•ΈοΈ Task Chain for deploy:prod:
  1. lint:types (TypeScript check)
  2. test:unit (Unit tests)
  3. build:prod (Production build)
  4. deploy:prod (Deploy to production)

πŸ’‘ Recommendation: Add test:e2e before deploy:prod

Smart Task Creation

# Describe what you want, get intelligent suggestions
> create_task(
    project_path="./my-app",
    task_description="Generate API documentation from OpenAPI spec"
  )

🧠 Analysis: Documentation generation task
πŸ“ Suggested Placement: docs:api
πŸ”— Dependencies: build:prod (for spec generation)
πŸ“ Suggested Implementation:
  [tasks.docs.api]
  run = "swagger-codegen generate -i ./openapi.json -l html2 -o ./docs/api"
  sources = ["src/api/**/*.ts", "openapi.json"]
  outputs = ["docs/api/**/*"]

Architecture Philosophy

Wise Mise MCP follows a Domain-Driven Design approach to task organization:

πŸ›οΈ Hierarchical Structure

  • Level 1: Domain (build, test, lint, etc.)
  • Level 2: Environment/Type (dev, prod, unit, e2e)
  • Level 3: Specific Implementation (server, client, api)

πŸ”„ Dependency Patterns

  • Sequential: lint β†’ test β†’ build β†’ deploy
  • Parallel: test:unit + test:e2e β†’ deploy
  • Conditional: deploy:staging β†’ test:smoke β†’ deploy:prod

⚑ Performance Optimization

  • Source Tracking: Only rebuild when sources change
  • Output Caching: Reuse previous build artifacts
  • Incremental Builds: Support for modern build tools

Technology Support

Wise Mise MCP includes expert knowledge for:

Frontend: React, Vue, Angular, Svelte, Next.js, Nuxt, Vite, Webpack Backend: Node.js, Python, Go, Rust, Java, .NET, PHP Databases: PostgreSQL, MySQL, MongoDB, Redis, Prisma, TypeORM Testing: Jest, Vitest, Cypress, Playwright, PyTest, Go Test CI/CD: GitHub Actions, GitLab CI, CircleCI, Jenkins Deployment: Docker, Kubernetes, Vercel, Netlify, AWS, GCP

Contributing

We welcome contributions! See our Contributing Guide for details.

Quick Start for Contributors

# Clone and setup with UV
git clone https://github.com/delorenj/wise-mise-mcp
cd wise-mise-mcp
uv sync

# Run tests
uv run pytest

# Format code
uv run black .
uv run ruff check --fix .

License

MIT License - see LICENSE for details.

Support


Built with ❀️ by Jarad DeLorenzo and the open source community

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured