adforge 0.1.1

AD Forge — data-driven solution architecture with live cost estimates

AD Forge

Runtime-agnostic MCP server for data-driven solution architecture, with bundled Hermes skills.

Works with any MCP stdio client (Claude Desktop, Hermes, OpenClaw, etc.). The core logic is also importable as plain Python packages.

Authors: Amirthanathan R ([e-mail]) and Dhivya GL ([e-mail]).

Project Structure

├── pyproject.toml              # Package metadata, deps, tool config
├── README.md                   # This file
├── CLAUDE.md                   # Guidance for Claude Code
├── .env.example                # Template for local env vars
├── Makefile                    # Common development targets
├── src/
│   └── adforge/
│       ├── __init__.py
│       ├── server.py           # MCP stdio server wiring
│       ├── models/             # Pydantic domain models
│       │   ├── problem.py
│       │   ├── solution.py
│       │   ├── sub_problem.py
│       │   ├── dag.py
│       │   └── cost.py
│       ├── persistence/        # SQLite repositories + migrations
│       │   ├── db.py
│       │   ├── migrations/
│       │   └── repositories/
│       ├── pricing/            # Cloud/role pricing provider wrappers
│       │   ├── aws.py
│       │   ├── azure.py
│       │   ├── gcp.py
│       │   ├── databricks.py
│       │   ├── roles.py
│       │   ├── cache.py (in repositories)
│       │   └── manager.py
│       ├── tools/              # MCP tool modules
│       │   ├── problem.py
│       │   ├── solution.py
│       │   ├── decompose.py
│       │   ├── cost.py
│       │   └── solution_cost.py
│       ├── prompts/            # MCP prompt modules
│       │   └── problem_decomposition.py
│       └── utils/
│           └── logging.py
├── skills/
│   └── solution-architect/
│       └── SKILL.md            # Hermes skill for architecture work
└── tests/

Quick Start

Install dependencies (uses uv or pip):

uv pip install -e ".[dev]"
# or
pip install -e ".[dev]"

Run the server manually:

python -m adforge.server
# or
adforge-mcp

Run tests:

uv run pytest tests/ -q

Workflow

1. Capture a problem — create_problem(title, description, tags_csv).
2. Decompose it — use the problem_decomposition prompt, then call decompose_problem(...) to create sub-problems and a DAG.
3. Create a solution — create_solution(problem_id, title, body).
4. Attach costs — attach_cost_to_solution(...) for each service/role/region.
5. Summarize — summarize_solution_cost(solution_id).
6. Review — add_solution_comment, add_solution_review, update_solution_status.

Pricing Sources

• aws — public AWS Price List Query API (us-east-1), no credentials required.
• azure — Azure Retail Prices API, no credentials required.
• gcp / databricks — stub providers; supply manual_rate in parameters_json.
• role — public/free role-pricing defaults; override with manual_rate.

Hermes Skill

The skills/solution-architect/SKILL.md is a Hermes-compatible skill. To use it:

• Copy or symlink it into ~/.hermes/skills/solution-architect/SKILL.md, or
• Keep it in-repo and load it directly if your agent supports in-repo skills.

MCP Configuration

Add to your MCP client config:

{
  "mcpServers": {
    "adforge": {
      "command": "uvx",
      "args": ["adforge"]
    }
  }
}

Or for local development:

mcp_servers:
  adforge:
    command: "uv"
    args: ["run", "python", "-m", "adforge.server"]

Environment Variables

Copy .env.example to .env and adjust:

• ADFORGE_DB_PATH — SQLite file path.
• ADFORGE_CACHE_TTL — pricing cache TTL in seconds.
• AWS_PRICING_REGION — AWS region for pricing queries.
• LOG_LEVEL, MCP_SERVER_NAME.