Spec-Driven Development
The Spec-Driven Development MCP Server facilitates a structured development workflow through a series of prompts. These prompts generate requirements, design documents, and then code.
The MCP server guides developers through a distinct three-step process. It begins with the definition of requirements via the EARS format, proceeds to the creation of a design document, and concludes with the generation of implementation code from that design.
This methodology establishes a traceable path from concept to product, which promotes consistency and helps teams meet project goals.
Installation
To use this MCP server, you should add its configuration to the mcp.json file in your project’s root directory. The MCP client reads this file to find and manage available servers.
{
"servers": {
"spec-driven": {
"command": "npx",
"args": [
"-y",
"mcp-server-spec-driven-development@latest"
]
}
}
}This configuration tells the MCP client to create a server named spec-driven. It uses npx to download and run the mcp-server-spec-driven-development package on demand.
Available Prompts
The server’s workflow is built around three core prompts:
1. Generate Requirements Document (generate-requirements)
- Description: Generates a
requirements.mdfile with the EARS format. - Input: A high-level text description of the application’s requirements. For example: ‘A Vue.js todo application with task creation, completion tracking, and local storage persistence’.
- Output: Creates a structured requirements document at
specs/requirements.md.
2. Generate Design from Requirements (generate-design-from-requirements)
- Description: Generates a
design.mdfile from the requirements. - Input: Reads directly from the
specs/requirements.mdfile. - Output: Creates a design document at
specs/design.md.
3. Generate Code from Design (generate-code-from-design)
- Description: Generates implementation code from the design document.
- Input: Reads directly from the
specs/design.mdfile. - Output: Places the generated implementation code in the project’s root folder.
You should run these prompts in order. This creates a clear and traceable path from the initial idea to the final code.
FAQs
Q: What is the EARS format?
A: EARS, or Easy Approach to Requirements Syntax, is a notation that structures requirement statements to reduce ambiguity. It uses specific keywords to define the conditions under which a system must perform an action. This server uses it to generate requirements that are clear, concise, and testable.
Q: Can I modify the generated documents or code?
A: Yes. The generated files (requirements.md, design.md, and the source code) are starting points. You can and should edit them to refine the details. The quality of the final output is directly influenced by the clarity of your initial requirements and any manual adjustments you make to the design document before you generate the code.
Q: What programming languages and frameworks does it support?
A: The server is language-agnostic. Its ability to generate specific code depends on the instructions inside the design document. If your design.md specifies a Python application with the Flask framework, it will attempt to generate that. The more detailed your design document, the more accurate the resulting code will be for your chosen tech stack.
Latest MCP Servers
CVE
WebMCP
webmcp is an MCP server that connects MCP clients to web search, page fetching, and local LLM-based extraction. It’s ideal…
Google Meta Ads GA4
Featured MCP Servers
Notion
Claude Peers
Excalidraw
FAQs
Q: What exactly is the Model Context Protocol (MCP)?
A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.
Q: How is MCP different from OpenAI's function calling or plugins?
A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.
Q: Can I use MCP with frameworks like LangChain?
A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.
Q: Why was MCP created? What problem does it solve?
A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.
Q: Is MCP secure? What are the main risks?
A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.
Q: Who is behind MCP?
A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.



