Skip to main content

MCP server for structured feature development through Requirements → Design → Tasks workflow

Project description

spec-server

An MCP (Model Context Protocol) server that provides structured feature development capabilities through a systematic three-phase workflow: Requirements → Design → Implementation Tasks.

Overview

spec-server helps AI assistants and developers transform rough feature ideas into executable implementation plans through a structured approach:

  1. Requirements Phase: Define user stories and acceptance criteria in EARS format
  2. Design Phase: Create comprehensive technical design documents
  3. Tasks Phase: Generate actionable implementation tasks with test-driven development focus

Features

  • Systematic Workflow: Enforced progression through Requirements → Design → Tasks phases
  • MCP Integration: Works with any MCP-compatible AI assistant
  • File References: Support for #[[file:path]] syntax to include external specifications
  • Task Management: Hierarchical task tracking with status updates
  • Multiple Transports: Support for both stdio and SSE (Server-Sent Events) transport methods
  • Validation: Built-in validation for document formats and workflow transitions

Installation

From PyPI

pip install spec-server

From Source

git clone https://github.com/teknologika/spec-server.git
cd spec-server
pip install -e .

Usage

As MCP Server

Add to your MCP client configuration:

{
  "mcpServers": {
    "spec-server": {
      "command": "spec-server",
      "args": ["stdio"],
      "disabled": false
    }
  }
}

Command Line

# Run with stdio transport (default)
spec-server

# Run with SSE transport on port 8000
spec-server sse 8000

# Run with SSE transport on custom port
spec-server sse 3000

MCP Tools

The server exposes the following MCP tools:

create_spec

Create a new feature specification with initial requirements document.

Parameters:

  • feature_name (string): Kebab-case feature identifier
  • initial_idea (string): User's rough feature description

update_spec_document

Update requirements, design, or tasks documents with workflow validation.

Parameters:

  • feature_name (string): Target spec identifier
  • document_type (enum): "requirements" | "design" | "tasks"
  • content (string): Updated document content
  • phase_approval (boolean): Whether user approves current phase

list_specs

List all existing specifications with their current status and progress.

read_spec_document

Retrieve content of spec documents with file reference resolution.

Parameters:

  • feature_name (string): Target spec identifier
  • document_type (enum): "requirements" | "design" | "tasks"

execute_task

Execute a specific implementation task from the tasks document.

Parameters:

  • feature_name (string): Target spec identifier
  • task_identifier (string): Task number/identifier

delete_spec

Remove a specification entirely including all documents.

Parameters:

  • feature_name (string): Target spec identifier

Workflow

1. Requirements Phase

  • Create user stories in "As a [role], I want [feature], so that [benefit]" format
  • Define acceptance criteria using EARS (Easy Approach to Requirements Syntax)
  • Must receive explicit approval before advancing to design phase

2. Design Phase

  • Generate comprehensive technical design based on approved requirements
  • Include sections: Overview, Architecture, Components, Data Models, Error Handling, Testing
  • Conduct research and incorporate findings into design decisions
  • Must receive explicit approval before advancing to tasks phase

3. Tasks Phase

  • Create actionable implementation tasks focused on code development
  • Format as numbered checkboxes with hierarchical structure
  • Reference specific requirements and ensure test-driven development
  • Tasks ready for execution by coding agents

File Structure

specs/
├── feature-name-1/
│   ├── requirements.md
│   ├── design.md
│   └── tasks.md
├── feature-name-2/
│   ├── requirements.md
│   └── design.md
└── .spec-metadata.json

File References

Spec documents support file references using the syntax #[[file:relative/path/to/file.md]]. These references are automatically resolved and their content is included when documents are read.

Example:

# API Design

The API follows the OpenAPI specification defined in:
#[[file:api/openapi.yaml]]

Development

Setup Development Environment

git clone https://github.com/teknologika/spec-server.git
cd spec-server
pip install -e ".[dev]"

Run Tests

pytest

Code Quality

# Format code
black src tests

# Sort imports
isort src tests

# Lint code
flake8 src tests

# Type checking
mypy src

Configuration

Workspace Integration

spec-server features intelligent workspace detection that automatically organizes your specifications with your project files when running in an IDE or project directory.

How It Works:

  • Automatic Detection: Scans upward from current directory looking for workspace indicators
  • Smart Placement: Creates .specs/ directory at the detected workspace root
  • Fallback Behavior: Uses specs/ directory in current location if no workspace detected
  • IDE Friendly: Works seamlessly with VS Code, IntelliJ, Sublime Text, and other editors

Workspace Indicators:

  • .git (Git repository)
  • package.json (Node.js project)
  • pyproject.toml (Python project)
  • Cargo.toml (Rust project)
  • go.mod (Go project)
  • pom.xml (Maven project)
  • .vscode, .idea (IDE configurations)
  • README.md, LICENSE, Makefile (common project files)

Example Structure:

my-project/                 ← Detected workspace root
├── .git/
├── src/
├── package.json
├── README.md
└── .specs/                 ← Specs automatically placed here
    ├── user-auth/
    │   ├── requirements.md
    │   ├── design.md
    │   └── tasks.md
    ├── data-export/
    │   └── requirements.md
    └── .spec-metadata.json

Benefits:

  • Version Control: Specs can be committed alongside your code
  • Team Collaboration: Shared specifications across team members
  • Context Awareness: Specs are logically grouped with related projects
  • IDE Integration: Specifications appear in your project file tree
  • Automatic Organization: No manual directory management required

Configuration:

# Enable/disable workspace detection (default: true)
export SPEC_SERVER_AUTO_DETECT_WORKSPACE=true

# Customize specs directory name in workspace (default: ".specs")
export SPEC_SERVER_WORKSPACE_SPECS_DIR=".my-specs"

# Customize fallback directory name (default: "specs")
export SPEC_SERVER_SPECS_DIR="my-specs"

Environment Variables

  • SPEC_SERVER_SPECS_DIR: Base directory for specs (default: "specs")
  • SPEC_SERVER_AUTO_DETECT_WORKSPACE: Enable workspace detection (default: "true")
  • SPEC_SERVER_WORKSPACE_SPECS_DIR: Specs directory name in workspace (default: ".specs")
  • SPEC_SERVER_PORT: Default SSE server port (default: 8000)
  • SPEC_SERVER_LOG_LEVEL: Logging level (default: "INFO")

Configuration File

Optional spec-server.json:

{
  "specs_dir": "specs",
  "auto_detect_workspace": true,
  "workspace_specs_dir": ".specs",
  "host": "127.0.0.1",
  "port": 8000,
  "transport": "stdio",
  "log_level": "INFO",
  "auto_backup": true,
  "cache_enabled": true
}

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

Roadmap

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

spec_server-0.5.0.tar.gz (135.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

spec_server-0.5.0-py3-none-any.whl (90.3 kB view details)

Uploaded Python 3

File details

Details for the file spec_server-0.5.0.tar.gz.

File metadata

  • Download URL: spec_server-0.5.0.tar.gz
  • Upload date:
  • Size: 135.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.11

File hashes

Hashes for spec_server-0.5.0.tar.gz
Algorithm Hash digest
SHA256 7852af0012e28aadceb92a0ab3a5a26807e6fc9216abd250abae7ddb26389376
MD5 a7229c30de644bfa82383ff28bb2bf92
BLAKE2b-256 ab470d0e277e69ce62be36999c6e1b475c205c7c7dda02ac3c4ec6b64adc740f

See more details on using hashes here.

File details

Details for the file spec_server-0.5.0-py3-none-any.whl.

File metadata

  • Download URL: spec_server-0.5.0-py3-none-any.whl
  • Upload date:
  • Size: 90.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.11

File hashes

Hashes for spec_server-0.5.0-py3-none-any.whl
Algorithm Hash digest
SHA256 70fcb8fb82f9b580b091240ec2066819a9bf3972c5be7470717383282d13aa32
MD5 bd689c8910aed7e0ad96076847776a25
BLAKE2b-256 5e481eb007527ce4bd901bb85220e77de44f37497598dfa30bfc9851b2c131e8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page