Agile Team MCP Server

An MCP server providing model wrapper tools for agile development teams, requiring external API keys.

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.

- 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

# 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

Create and edit your.envfile with your API keys:

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

# 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

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

Clients tend to have slighty different configurations

{ "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" } } } }

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

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)"

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

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

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

# 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.

Tools to discover available LLM providers and their supported models.

Lists all supported LLM providers and their shortcut prefixes.

Lists all available models for a specific provider.

# Simple example with full provider name list_models_tool: "openai" # Using provider shortcode list_models_tool: "a" # Lists Anthropic models

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

- Send prompts to one or multiple models simultaneously
- Use model suffixes for special behaviors:

- :4kor other numbers for thinking token budgets
- :highfor increased reasoning effort (OpenAI only)

# 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"]

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

# 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"]

# 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"

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

# 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"

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

- 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

# 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"

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

- 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

# 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"

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

- 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

# 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"

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