
MCP Agile Flow
A comprehensive system for managing AI-assisted agile development workflows with a modern, resource-based API using FastMCP.
Tools
get_project_settings
Get the project settings for the current working directory or a proposed path. Returns configuration settings including project path, type, and metadata. If proposed_path is not provided or invalid, uses the current directory.
think
Record a thought for later reference and analysis. This tool allows you to record thoughts during development or analysis processes. Thoughts can be organized by category and depth to create a hierarchical structure of analysis.
get_thoughts
Retrieve recorded thoughts. This tool retrieves all previously recorded thoughts, optionally filtered by category. You can also choose to organize them hierarchically by depth.
clear_thoughts
Clear recorded thoughts. This tool removes previously recorded thoughts, optionally filtered by category. If no category is specified, all thoughts will be cleared.
get_thought_stats
Get statistics about recorded thoughts. This tool provides statistics about recorded thoughts, such as count and depth distribution. Results can be filtered by category.
detect_thinking_directive
Detect thinking directives. This tool analyzes text to detect directives suggesting deeper thinking, such as "think harder", "think deeper", "think again", etc.
should_think
Assess whether deeper thinking is needed for a query. This tool analyzes a query to determine if it requires deeper thinking, based on complexity indicators and context.
think_more
Get guidance for thinking more deeply. This tool provides suggestions and guidance for thinking more deeply about a specific query or thought.
initialize_ide
Initialize IDE project structure with appropriate directories and config files. This tool sets up the necessary directories and configuration files for IDE integration, including .ai-templates directory and IDE-specific rules. Note: If project_path is omitted, not a string, invalid, or the directory doesn't exist, the current working directory will be used automatically.
initialize_ide_rules
Initialize IDE rules for a project. This tool sets up IDE-specific rules for a project, creating the necessary files and directories for AI assistants to understand project conventions. Note: If project_path is omitted, not a string, or invalid, the current working directory will be used automatically.
prime_context
Prime project context by analyzing documentation and structure. This tool analyzes the project structure and documentation to provide context information for AI assistants working with the project. Note: If project_path is omitted, not a string, or invalid, the current working directory will be used automatically.
migrate_mcp_config
Migrate MCP configuration between different IDEs. This tool helps migrate configuration and rules between different IDEs, ensuring consistent AI assistance across different environments. Note: If project_path is omitted, not a string, or invalid, the current working directory will be used automatically.
process_natural_language
Process natural language command and route to appropriate tool. This tool takes a natural language query and determines which tool to call with what parameters, providing a way to interact with the MCP Agile Flow tools using natural language.
README
MCP Agile Flow
A comprehensive system for managing AI-assisted agile development workflows with a modern, resource-based API using FastMCP.
Status
✅ Migration Complete: The migration from legacy server to FastMCP implementation is fully complete. All legacy code and tests have been removed.
Overview
The MCP Agile Flow project uses a resource-based approach with FastMCP from the official MCP SDK, focusing on:
- RESTful API Design - Clean, intuitive resource URIs for data access
- Resource-First Architecture - Optimized for data retrieval and state representation
- Action-Oriented Tools - Tools used only for operations that modify state
Key Features
- Agile Documentation: Generate and maintain comprehensive AI documentation
- Project Structure: Organize your project with AI-generated files and directories
- IDE Integration: Direct integration with various AI IDEs (Cursor, Windsurf, Cline)
- Workflow Management: Track agile stories and progress
- Intuitive API Structure: Resources organized in a RESTful hierarchy
- Simplified Integration: Direct mapping to resource URIs
- Improved Performance: Optimized for data access patterns
Getting Started
To use MCP Agile Flow:
-
Install the package:
pip install mcp-agile-flow
-
Import in your code:
from mcp_agile_flow import call_tool, call_tool_sync # Use async interface result = await call_tool("get-project-settings", {}) # Or use sync interface result = call_tool_sync("get-project-settings", {})
MCP Client Configuration
Important: Configuration Update Required
If you had previously configured MCP Agile Flow, you need to update your configuration. The fastmcp_server.py
module has been removed as part of code cleanup, and functionality has been consolidated into the main package.
Update your MCP client configuration from:
{
"name": "mcp-agile-flow",
"server": {
"type": "module",
"module": "mcp_agile_flow.fastmcp_server",
"entry_point": "run"
}
}
To:
{
"name": "mcp-agile-flow",
"server": {
"type": "module",
"module": "mcp_agile_flow",
"entry_point": "main"
}
}
For Cursor users, also update the mcp.json file (typically at ~/.cursor/mcp.json):
"mcp-agile-flow": {
"command": "/path/to/python",
"args": [
"-m",
"mcp_agile_flow" // Updated from "mcp_agile_flow.fastmcp_server"
],
"autoApprove": [
// ...
]
}
Command Line Usage
You can also run the server directly from the command line:
# Using Python (logs disabled by default)
python -m mcp_agile_flow
# Enable normal logging
python -m mcp_agile_flow --verbose
# Debug mode (most verbose logging)
python -m mcp_agile_flow --debug
Available Tools
The MCP Agile Flow provides several tools:
get-project-settings
: Get project settings including paths and environment variablesinitialize-ide
: Initialize project directory structure for specific IDEsinitialize-ide-rules
: Initialize AI rule files for specific IDEsprime-context
: Analyze project documentation and build contextual understandingmigrate-mcp-config
: Migrate MCP configuration between different IDEsthink
: Record a thought for complex reasoning and step-by-step analysisget-thoughts
: Retrieve all thoughts recorded in the current sessionclear-thoughts
: Clear all recorded thoughts from the current sessionget-thought-stats
: Get statistics about the thoughts recorded in the current sessionprocess-natural-language
: Process natural language commands and route to appropriate tools
Natural Language Commands
MCP Agile Flow supports natural language commands, making it easier to interact with the tools without remembering exact command names. Simply use conversational phrases, and the system will automatically detect your intent and map them to the appropriate tools with the correct parameters.
Supported Command Types
Migration Commands
To migrate MCP configuration between different IDEs:
- "migrate mcp config to claude-desktop"
- "migrate config from cursor to claude-desktop"
- "copy mcp settings to windsurf"
- "transfer config to cline"
- "move mcp settings from cursor to roo"
If the source IDE is not specified, it defaults to "cursor".
Note: Valid IDE names are: "cursor", "windsurf-next", "windsurf", "cline", "roo", and "claude-desktop".
Initialization Commands
To initialize a project with rules for a specific IDE:
- "initialize ide for claude"
- "setup rules for windsurf"
- "create ide for cline"
- "initialize rules for copilot"
Project Settings Commands
To get comprehensive project settings:
- "get project settings"
- "show settings"
- "project settings"
Context Analysis Commands
To analyze project documentation:
- "prime context"
- "analyze project context"
- "build context"
Thinking Commands
To record a thought:
- "think about [your thought here]"
Usage Examples
Here are some examples of how to use these commands:
from mcp_agile_flow import process_natural_language
# Migrate configuration from Cursor to Claude
result = process_natural_language("migrate mcp config to claude-desktop")
# Initialize rules for Windsurf
result = process_natural_language("initialize ide for windsurf")
# Get project settings
result = process_natural_language("get project settings")
# Prime the context
result = process_natural_language("prime context")
# Record a thought
result = process_natural_language("think about how to improve code quality")
Using from Command Line
You can also use natural language commands with the MCP Agile Flow CLI:
python -m mcp_agile_flow process-natural-language "migrate mcp config to claude-desktop"
Error Handling
If the system cannot recognize a command, it will return an error message explaining that no command was detected and suggesting to use more specific wording.
Extending Commands
The natural language command detection is implemented in utils.py
using regular expressions. To add support for new command patterns, add appropriate regex patterns to the detect_mcp_command
function.
Development
To set up for development:
-
Clone the repository:
git clone https://github.com/yourusername/mcp-agile-flow.git cd mcp-agile-flow
-
Create a virtual environment:
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate
-
Install development dependencies:
pip install -e ".[dev]"
-
Run tests:
pytest
-
Common Makefile commands:
make test # Run all tests make test-nl-commands # Test natural language command functionality make test-core # Run core tests only make coverage # Generate coverage report make clean # Clean build artifacts make clean-all # Clean everything including venv make clean-archived # Remove archived legacy files
License
This project is licensed under the MIT License - See LICENSE file for details.
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