Agile Team MCP Server

A team of Agent Personas wrapped in an MCP server that has the ability to leverage at scale massive compute by wrapping various LLM providers to perform activities as an Agile Team Persona.

Features

Model Wrapping: Send prompts to multiple LLM models with a unified interface
Provider/Model Correction: Automatically correct and validate provider and model names
File Support: Send prompts from files and save responses to files
Provider/Model Discovery: List available providers and models
Persona Tools: Specialized personas like Business Analyst, Product Manager, Spec Writer, and Team Decision Maker

Setup

Installation

# Clone and install
git clone https://github.com/danielscholl/agile-team-mcp-server.git
cd agile-team-mcp-server
uv sync

# Install
uv pip install -e .

# Run tests to verify installation
uv run pytest

Environment Configuration

Create and edit your .env file with your API keys:

# Create environment file from template
cp .env.sample .env

Required API keys in your .env file:

# Required API keys
OPENAI_API_KEY=your_openai_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here  # For Google Gemini models
GROQ_API_KEY=your_groq_api_key_here
DEEPSEEK_API_KEY=your_deepseek_api_key_here
OLLAMA_HOST=http://localhost:11434

# Optional model configuration
DEFAULT_MODEL=openai:gpt-4o-mini
DEFAULT_TEAM_MODELS=["openai:gpt-4.1","anthropic:claude-3-7-sonnet","gemini:gemini-2.5-pro"]
DEFAULT_DECISION_MAKER_MODEL=openai:gpt-4o-mini

MCP Server Configuration

To utilize this MCP server directly in other projects either use the buttons to install in VSCode, edit the .mcp.json file directory.

Clients tend to have slighty different configurations

Configure for Claude.app

{
  "mcpServers": {
    "agile-team": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/danielscholl/agile-team-mcp-server@main",
        "agile-team"
      ],
      "env": {
        "OPENAI_API_KEY": "<YOUR_OPENAI_KEY>",
        "ANTHROPIC_API_KEY": "<YOUR_ANTHROPIC_KEY>",
        "GEMINI_API_KEY": "<YOUR_GEMINI_KEY>",
        "GROQ_API_KEY": "<YOUR_GROQ_KEY>",
        "DEEPSEEK_API_KEY": "<YOUR_DEEPSEEK_KEY>",
        "OLLAMA_HOST": "http://localhost:11434",
        "DEFAULT_MODEL": "openai:gpt-4o-mini",
        "DEFAULT_TEAM_MODELS": "[\"openai:gpt-4.1\",\"anthropic:claude-3-7-sonnet\",\"gemini:gemini-2.5-pro\"]",
        "DEFAULT_DECISION_MAKER_MODEL": "openai:gpt-4o-mini"
      }
    }
  }
}

Configure for Claude.code

Setting up Agile Team with Claude Code easily by importing it.

claude mcp add-from-claude-desktop

Note: "--directory" would be the path to the source code if not in the same directory.

# Copy this JSON configuration
{
    "command": "uvx",
    "args": ["--from", "git+https://github.com/danielscholl/agile-team-mcp-server@main", "agile-team"],
    "env": {
        "DEFAULT_MODEL": "openai:gpt-4o-mini",
        "DEFAULT_TEAM_MODELS": "[\"openai:gpt-4.1\",\"anthropic:claude-3-7-sonnet\",\"gemini:gemini-2.5-pro\"]",
        "DEFAULT_DECISION_MAKER_MODEL": "openai:gpt-4o-mini"
    }
}

# Then run this command in Claude Code
claude mcp add agile-team "$(pbpaste)"

To remove the configuration later:

claude mcp remove agile-team

Available LLM Providers

Provider	Short Prefix	Full Prefix	Example Usage
OpenAI	`o`	`openai`	`o:gpt-4o-mini`
Anthropic	`a`	`anthropic`	`a:claude-3-5-haiku`
Google Gemini	`g`	`gemini`	`g:gemini-2.5-pro-exp-03-25`
Groq	`q`	`groq`	`q:llama-3.1-70b-versatile`
DeepSeek	`d`	`deepseek`	`d:deepseek-coder`
Ollama	`l`	`ollama`	`l:llama3.1`

Usage

Command Line

Run the server directly:

uv run agile-team

With MCP Client

With a compatible MCP client, you can connect to the server:

mcp use agile-team

Available Prompts

Interactive conversation starters and guided workflows to help you discover and use server capabilities.

List MCP Assets

Get a comprehensive overview of all server capabilities including tools, personas, providers, and workflows.

Parameters: None required

Usage:

# Get complete server capability overview
list_mcp_assets

Returns: Comprehensive markdown documentation including:

All available tools with parameters and examples
Supported LLM providers with shortcuts and usage examples
Agent personas (Business Analyst, Product Manager, Spec Writer, Decision Maker)
Quick start workflows for agile team processes
Advanced usage patterns and best practices
Pro tips for model selection and workflow optimization

This prompt provides a self-documenting overview of the entire agile-team MCP server, making it easy to discover capabilities and get started with productive workflows.

Available Tools

List Available Options

Tools to discover available LLM providers and their supported models.

List Providers Tool

Lists all supported LLM providers and their shortcut prefixes.

Parameters: None required

Examples:

# Simple example
list_providers_tool

List Models Tool

Lists all available models for a specific provider.

Parameters:

Parameter	Description	Default Value
`provider`	The provider to list models for (e.g., "openai", "anthropic")	required

Examples:

# Simple example with full provider name
list_models_tool: "openai"

# Using provider shortcode
list_models_tool: "a"  # Lists Anthropic models

Send Prompts to Models

Send text prompts directly to LLM models and get their responses.

Parameters:

Parameter	Description	Default Value
`text`	The prompt text to send to the models	required
`models_prefixed_by_provider`	List of models in format "provider:model"	`openai:gpt-4o-mini`

Features:

Send prompts to one or multiple models simultaneously
Use model suffixes for special behaviors:
- :4k or other numbers for thinking token budgets
- :high for increased reasoning effort (OpenAI only)

Examples:

# Simple example
prompt_tool: "Create a plan for implementing user authentication"

# Complex example with multiple models and options
prompt_tool: "Analyze the trade-offs between microservices and monoliths" ["openai:gpt-4.1:high", "anthropic:claude-3-7-sonnet:4k"]

Work with Files

Process prompts from files and save responses to files for batch processing.

From File Tool

Parameters:

Parameter	Description	Default Value
`file_path`	Path to the file containing the prompt	required
`models_prefixed_by_provider`	List of models in format "provider:model"	`openai:gpt-4o-mini`

Examples:

# Simple example
prompt_from_file_tool: "prompts/function.md"

# Complex example with specific model
prompt_from_file_tool: "prompts/function.md" ["anthropic:claude-3-7-sonnet-20250219"]

From File to File Tool

Parameters:

Parameter	Description	Default Value
`file_path`	Path to the file containing the prompt	required
`models_prefixed_by_provider`	List of models in format "provider:model"	`openai:gpt-4o-mini`
`output_path`	Full path for the output file	Generated based on input
`output_dir`	Directory for response files	input file's directory/responses
`output_extension`	File extension for output files	`md`

Examples:

# Simple example
prompt_from_file2file_tool: "prompts/uv_script.md"

# Complex example with specific model, output path and custom extension
prompt_from_file2file_tool: "prompts/diagram.md" ["anthropic:claude-3-7-sonnet"] "prompts/responses/architecture_diagram.md"

Team Decision Making

Use multiple models as team members to generate different solutions, then have a decision maker model evaluate and choose the best approach.

Parameters:

Parameter	Description	Default Value
`from_file`	Path to the file containing the prompt	required
`models_prefixed_by_provider`	List of team member models	`["openai:gpt-4.1", "anthropic:claude-3-7-sonnet", "gemini:gemini-2.5-pro"]`
`persona_dm_model`	Model for making the decision	`openai:gpt-4o-mini`
`output_path`	Full path for the output document	Generated based on input
`output_dir`	Directory for response files	input file's directory/responses
`output_extension`	File extension for output files	`md`
`persona_prompt`	Custom decision maker prompt	Default template

Examples:

# Simple example
persona_dm_tool: "prompts/decision.md"

# Complex example with custom team and decision maker model
persona_dm_tool: "prompts/decision.md" ["o:gpt-4.1", "a:claude-3-7-sonnet", "g:gemini-2.5-pro-preview-03-25"] persona_dm_model="o:o3" "prompts/responses/final_decision.md"

Business Analyst Persona

Generate detailed business analysis using a specialized Business Analyst persona, with optional team-based decision making.

Capabilities:

Creating detailed project briefs and requirement documents
Analyzing business needs and market opportunities
Defining MVP scope and feature prioritization
Identifying target audiences and user personas

Parameters:

Parameter	Description	Default Value
`from_file`	Path to the file containing business requirements	required
`models_prefixed_by_provider`	Models to use in format "provider:model"	`openai:gpt-4o-mini`
`output_path`	Full path for the output document	Generated based on input
`output_dir`	Directory for response files	input file's directory/responses
`output_extension`	File extension for output files	`md`
`use_decision_maker`	Whether to use team decision making	`false`
`decision_maker_models`	Models for team members if using decision maker	`["openai:gpt-4.1", "anthropic:claude-3-7-sonnet", "gemini:gemini-2.5-pro"]`
`decision_maker_model`	Model for final decision making	`openai:gpt-4o-mini`

Examples:

# Simple example
persona_ba_tool: "prompts/concept.md" "prompts/responses/project-brief.md"

# Complex example with team-based decision making
persona_ba_tool: "prompts/concept.md" use_decision_maker=true decision_maker_model="o:04-mini" "prompts/responses/project-brief.md"

Product Manager Persona

Generate comprehensive product management plans using a specialized Product Manager persona, with optional team-based decision making.

Capabilities:

Creating detailed product plans with prioritized features and clear timelines
Developing product vision and strategy
Performing market and competitive analysis
Defining user stories and requirements
Managing cross-functional team collaboration
Implementing data-driven decision making

Parameters:

Parameter	Description	Default Value
`from_file`	Path to the file containing the product requirements	required
`models_prefixed_by_provider`	Models to use in format "provider:model"	`openai:gpt-4o-mini`
`output_path`	Full path for the output document	Generated based on input
`output_dir`	Directory for response files	input file's directory/responses
`output_extension`	File extension for output files	`md`
`use_decision_maker`	Whether to use team decision making	`false`
`decision_maker_models`	Models for team members if using decision maker	`["openai:gpt-4.1", "anthropic:claude-3-7-sonnet", "gemini:gemini-2.5-pro"]`
`decision_maker_model`	Model for final decision making	`openai:gpt-4o-mini`
`pm_prompt`	Custom Product Manager prompt template	Default template
`decision_maker_prompt`	Custom decision maker prompt template	Default template

Examples:

# Simple example
persona_pm_tool: "prompts/responses/project-brief.md" "prompts/responses/project-prd.md"

# Complex example with team-based decision making
persona_pm_tool: "prompts/responses/project-brief.md" use_decision_maker=true decision_maker_model="o:gpt-4o-mini" "prompts/responses/project-prd.md"

Spec Writer Persona

Generate clear, developer-ready specification documents from PRDs, project briefs, or user requests using a specialized Spec Writer persona.

Capabilities:

Producing technical specifications from PRDs or project briefs
Defining step-by-step implementation instructions for developers and AI agents
Creating comprehensive specifications with architectural patterns and validation criteria
Defining tool behavior, CLI structure, directory layout, and testing plans
Using focused, reproducible examples to communicate architectural patterns
Ensuring each spec includes validation steps to verify implementation

Parameters:

Parameter	Description	Default Value
`from_file`	Path to the file containing requirements or PRD	required
`models_prefixed_by_provider`	Models to use in format "provider:model"	`openai:gpt-4o-mini`
`output_path`	Full path for the output document	Generated based on input
`output_dir`	Directory for response files	input file's directory/responses
`output_extension`	File extension for output files	`md`
`use_decision_maker`	Whether to use team decision making	`false`
`decision_maker_models`	Models for team members if using decision maker	`["openai:gpt-4.1", "anthropic:claude-3-7-sonnet", "gemini:gemini-2.5-pro"]`
`decision_maker_model`	Model for final decision making	`openai:gpt-4o-mini`
`sw_prompt`	Custom Spec Writer prompt template	Default template
`decision_maker_prompt`	Custom decision maker prompt template	Default template

Examples:

# Simple example - generate a specification from a PRD
persona_sw_tool: "prompts/responses/project-prd.md" "prompts/responses/project-spec.md"

# Complex example with team-based decision making
persona_sw_tool: "prompts/responses/project-prd.md" use_decision_maker=true decision_maker_model=["o:gpt-4o-mini"] "prompts/responses/project-spec.md"