Supercharge Your AI Assistant or IDE with CodeRide Task Management
<a href="https://glama.ai/mcp/servers/@PixdataOrg/coderide-mcp">
<img width="300" height="157" src="https://glama.ai/mcp/servers/@PixdataOrg/coderide-mcp/badge" alt="coderide MCP server" />
</a>
<p align="center">
<a href="https://coderide.ai" target="_blank">
<img src="https://ideybnueizkxwqmjowpy.supabase.co/storage/v1/object/sign/coderide-website/Coderide-og-Facebook.jpg?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6InN0b3JhZ2UtdXJsLXNpZ25pbmcta2V5X2M5OWNmMjY4LTg5MTMtNGFiOS1iYjhhLTIxMTUyNDZjNGM2NCJ9.eyJ1cmwiOiJjb2RlcmlkZS13ZWJzaXRlL0NvZGVyaWRlLW9nLUZhY2Vib29rLmpwZyIsImlhdCI6MTc0ODM3ODg1MiwiZXhwIjoxNzc5OTE0ODUyfQ.jBb-x5f2MACBNBsls0u_9seYIiynektHqef2Y_vSMHQ" alt="CodeRide" width="100%"/>
</a>
</p>
<!-- Suggestion: Add badges here: npm version, license, build status, etc. -->
Give your AI coding sidekick the power of CodeRide! CodeRide MCP connects your favorite AI development tools (like Cursor, Cline, Windsurf, and other MCP clients) directly to CodeRide, the AI-native task management system.
Imagine your AI not just writing code, but truly understanding project context, managing its own tasks, and collaborating seamlessly with you. No more endless copy-pasting or manual updates. With CodeRide MCP, your AI becomes a first-class citizen in your CodeRide workflow.
🚀 Why CodeRide MCP is a Game-Changer
- Deep Project Understanding for Your AI: Equip your AI agents with rich, structured context from your CodeRide projects and tasks. Let them see the bigger picture.
- Seamless AI-Powered Task Automation: Empower AIs to fetch, interpret, and update tasks directly in CodeRide, automating routine project management.
- Bridge the Gap Between Human & AI Developers: Foster true collaboration with smoother handoffs, consistent task understanding, and aligned efforts.
- Optimized for LLM Efficiency: Compact JSON responses minimize token usage, ensuring faster, more cost-effective AI interactions.
- Secure by Design: Workspace-scoped API key authentication ensures your data's integrity and that AI operations are confined to the correct project context.
- Plug & Play Integration: Effortlessly set up with
npx in any MCP-compatible environment. Get your AI connected in minutes!
- Future-Proof Your Workflow: Embrace an AI-native approach to development, built on the open Model Context Protocol standard.
✨ Core Capabilities
The CodeRide MCP server provides your AI with the following capabilities:
- Task Management: Fetch specific tasks, list all tasks in a project, and get the next task in sequence.
- Task Updates: Modify task descriptions and statuses.
- Prompt Access: Get tailored prompts and instructions for specific tasks.
- Project Management: List all projects, retrieve project details, and manage project knowledge.
- Project Knowledge Management: Update a project's knowledge graph and architecture diagrams.
- Project Initiation: Get the first task of a project to kickstart work.
- Workflow Automation: Navigate through task sequences with smart next-task suggestions.
⚙️ Getting Started
Installing via Smithery (Recommended)
To install Coderide MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @PixdataOrg/coderide --client claude
Smithery Deployment Modes
CodeRide MCP supports dual-mode operation for both development and production use:
🔧 Development/Testing Mode (Mock)
Perfect for exploring features, testing integrations, or contributing to the project without needing a real CodeRide account.
How to activate: In the Smithery playground or configuration, either:
- Leave the
CODERIDE_API_KEY field empty
- Provide any placeholder value (e.g.,
mock-key, test, etc.)
What you get:
- All 9 tools available with realistic mock data
- Sample projects (ABC, XYZ) and tasks (ABC-1, ABC-2, etc.)
- Full MCP functionality for testing and development
- No real API calls - completely safe for experimentation
🚀 Production Mode (Real API)
For actual CodeRide users who want to integrate with their real projects and tasks.
How to activate: Provide a valid CodeRide API key that starts with CR_API_KEY_
What you get:
- Full integration with your CodeRide workspace
- Real project and task data
- Ability to update tasks and projects
- Complete workflow automation
Traditional MCP Configuration
For non-Smithery deployments, add this configuration to your MCP client:
{
"mcpServers": {
"CodeRide": {
"command": "npx",
"args": ["-y", "@coderide/mcp"],
"env": {
"CODERIDE_API_KEY": "YOUR_CODERIDE_API_KEY_HERE"
}
}
}
}
Prerequisites:
- Node.js and npm: Ensure you have Node.js (which includes npm) installed.
- CodeRide Account & API Key (Production only): For production use, you'll need an active CodeRide account and API key from app.coderide.ai.
Once configured, your MCP client will automatically connect to the CodeRide MCP server with the appropriate mode based on your configuration.
🤖 Who is this for?
CodeRide MCP is for:
- Developers using AI coding assistants: Integrate your AI tools (Cursor, Cline, Windsurf, etc.) deeply with your CodeRide task management.
- Teams adopting AI-driven development: Standardize how AI agents access project information and contribute to tasks.
- Anyone building with MCP: Leverage a powerful example of an MCP server that connects to a real-world SaaS platform.
If you're looking to make your AI assistant a more productive and integrated member of your development team, CodeRide MCP is for you.
🔨 Available Tools
Here's a breakdown of the tools provided by CodeRide MCP and how they can be used:
get_task
Retrieves detailed information about a specific task by its number (e.g., "TCA-3").
Input Schema:
{
"number": "task-number (e.g., 'TCA-3')",
"status": "to-do|in-progress|done",
}
Example Use Case:
- User Prompt: "Hey AI, what are the details for task APP-101?"
- AI Action: Calls
get_task with arguments: { "number": "APP-101" }.
- Outcome: AI receives the title, description, status, priority, and other context for task APP-101.
update_task
Updates an existing task's description, status, or other mutable fields.
Input Schema:
{
"number": "task-number-identifier",
"description": "updated-task-description",
"status": "to-do|in-progress|done"
}
Example Use Case:
- User Prompt: "AI, please mark task BUG-42 as 'done' and add a note: 'Fixed the off-by-one error.'"
- AI Action: Calls
update_task with arguments: { "number": "BUG-42", "status": "done", "description": "Fixed the off-by-one error." }.
- Outcome: Task BUG-42 is updated in CodeRide.
get_prompt
Retrieves the specific prompt or instructions tailored for an AI agent to work on a given task.
Input Schema:
{
"number": "task-number (e.g., 'TCA-3')"
}
Example Use Case:
- User Prompt: "AI, I'm ready to start on task ETF-7. What's the main objective?"
- AI Action: Calls
get_prompt with arguments: { "slug": "ETF", "number": "ETF-7" }.
- Outcome: AI receives the specific, actionable prompt for FEAT-7, enabling it to begin work with clear direction.
get_project
Retrieves details about a specific project using its slug.
Input Schema:
{
"slug": "project-slug (e.g., 'TCA')",
"name": "optional-project-name"
}
Example Use Case:
- User Prompt: "AI, can you give me an overview of the 'Omega Initiative' project?"
- AI Action: Calls
get_project with arguments: { "slug": "omega-initiative" }.
- Outcome: AI receives the project's name, description, and potentially links to its knowledge base or diagrams.
update_project
Updates a project's high-level information, such as its knowledge graph or system architecture diagram.
Input Schema:
{
"slug": "project-slug-identifier",
"project_knowledge": { },
"project_diagram": "/* Mermaid diagram string or similar */"
}
Example Use Case:
- User Prompt: "AI, I've updated the user authentication flow. Please update the project diagram for project 'APB'."
- AI Action: (After generating/receiving the new diagram) Calls
update_project with arguments: { "slug": "APB", "project_diagram": "/* new mermaid diagram */" }.
- Outcome: The 'AlphaProject' in CodeRide now has the updated architecture diagram.
start_project
Retrieves the first or next recommended task for a given project, allowing an AI to begin work.
Input Schema:
{
"slug": "project-slug (e.g., 'TCA')"
}
Example Use Case:
- User Prompt: "AI, let's get started on the 'MobileAppV2' project. What's the first task?"
- AI Action: Calls
start_project with arguments: { "slug": "MBC" }.
- Outcome: AI receives details for the initial task in the 'MBC' project, ready to begin.
list_projects ✨ NEW
Lists all projects in the user's workspace, providing an overview of available projects with intelligent workflow guidance.
Input Schema:
{
}
Example Use Case:
- User Prompt: "AI, show me all my projects."
