flowchart TD
    A[AI Agent / CI / Human] -->|tool call| S[Generated Tool Schema]
    S --> AD[Adapter layer<br/>openai · anthropic · langchain · llamaindex · mcp · cli]
    AD -->|typed args| V[Validation & sanitization<br/>paths · limits · rules]
    V -->|invalid| E[Structured error envelope<br/>stable code + details]
    V -->|valid| C[Core engine<br/>ast parse → graph → rule eval]
    C --> R[Response envelope<br/>violations + summary]
    C -->|failure| E
    E --> AD
    R --> AD
    AD --> A

# 1. Install
pip install archvanguard-mcp

# 2. Write a ruleset (rules.json)
cat > rules.json <<'JSON'
{
  "layers": [
    {"name": "core", "patterns": ["myapp.core", "myapp.core.*"]},
    {"name": "adapters", "patterns": ["myapp.adapters", "myapp.adapters.*"]}
  ],
  "allowed_dependencies": [{"from_layer": "adapters", "to_layer": "core"}],
  "forbidden_imports": [{"importer": "myapp.core.*", "forbidden": "langchain*"}],
  "allow_cycles": false
}
JSON

# 3. Enforce (exit code 1 if any violation — use it as a merge gate)
archvanguard --root ./src/myapp --rules rules.json

from openai import OpenAI
from archvanguard_mcp.adapters import openai_tool

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Check ./src/myapp against rules.json"}],
    tools=[openai_tool.tool_spec()],
)
for call in response.choices[0].message.tool_calls or []:
    tool_message = openai_tool.run_tool_call(call)  # dispatches to the engine
    # append tool_message to your messages and continue the loop

import anthropic
from archvanguard_mcp.adapters import anthropic_tool

client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    tools=[anthropic_tool.tool_spec()],
    messages=[{"role": "user", "content": "Verify the layering of ./src/myapp"}],
)
for block in message.content:
    if block.type == "tool_use":
        tool_result = anthropic_tool.handle_tool_use(block)  # -> tool_result block

from archvanguard_mcp.adapters import langchain_tool

tool = langchain_tool.build_tool()  # a StructuredTool with args_schema
# agent = create_react_agent(llm, [tool]) ; agent.invoke(...)

from archvanguard_mcp.adapters import llamaindex_tool

tool = llamaindex_tool.build_tool()  # a FunctionTool
# agent = ReActAgent.from_tools([tool], llm=llm) ; agent.chat(...)

{
  "mcpServers": {
    "archvanguard": {
      "command": "python",
      "args": ["-m", "archvanguard_mcp.adapters.mcp_server"]
    }
  }
}

pip install -e ".[all,dev]" && pre-commit install
make all   # lint + type + coverage(≥90%) + schema + security + build

@software{fatih_archvanguard_mcp_2026,
  author  = {Farhang Fatih},
  title   = {ArchVanguard-MCP: Framework-agnostic architecture-rule enforcement for AI agents},
  year    = {2026},
  version = {0.1.0},
  license = {MIT},
  url     = {https://github.com/MrGuevara4/ArchVanguard-MCP}
}