QWED MCP examples for verified tool execution

{
  "mcpServers": {
    "qwed-verification": {
      "command": "qwed-mcp",
      "env": {
        "QWED_MCP_TRUSTED_CODE_EXECUTION": "true"
      }
    }
  }
}

{
  "code": "from decimal import Decimal, ROUND_HALF_UP\n\nP = Decimal('10000')\nr = Decimal('0.05')\nn = Decimal('12')\nt = Decimal('10')\n\nFV = P * (1 + r/n) ** (n*t)\nFV = FV.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\nprint(f'Future value: ${FV}')"
}

STDOUT:
Future value: $16470.09

Execution completed successfully.

{
  "code": "from qwed_new.engines.logic_engine import verify_logic_statement\n\nresult = verify_logic_statement(\n    premises=[\n        'if agreed and consideration then contract',\n        'Alice agreed',\n        'consideration exchanged'\n    ],\n    conclusion='contract formed'\n)\nprint(f'Verified: {result.get(\"verified\")}')\nprint(f'Message: {result.get(\"message\", \"\")}')"
}

def login(username, password):
    query = f"SELECT * FROM users WHERE name='{username}'"
    user = db.execute(query)
    if eval(f"'{password}' == '{user.password}'"):
        return True
    return False

{
  "code": "from qwed_new.guards.code_guard import CodeGuard\n\ncode = '''def login(username, password):\n    query = f\"SELECT * FROM users WHERE name='{username}'\"\n    user = db.execute(query)\n    if eval(f\"'{password}' == '{user.password}'\"):\n        return True\n    return False'''\n\nguard = CodeGuard()\nresult = guard.verify_safety(code)\nprint(f'Verified: {result[\"verified\"]}')\nif result.get('violations'):\n    for v in result['violations']:\n        print(f'  - {v}')"
}

STDOUT:
Verified: False
  - Dangerous function call: eval()
  - Warning: String concatenation in SQL query detected

Execution completed successfully.

def login(username, password):
    query = "SELECT * FROM users WHERE name = ?"
    user = db.execute(query, (username,))
    return password == user.password

{
  "code": "from qwed_new.engines.logic_engine import verify_logic_statement\n\nresult = verify_logic_statement(\n    premises=[\n        'if p_value less than 0.05 then reject_null',\n        'p_value less than 0.05'\n    ],\n    conclusion='reject_null'\n)\nprint(f'Verified: {result.get(\"verified\")}')"
}

{
  "code": "from qwed_new.guards.sql_guard import SQLGuard\n\nguard = SQLGuard()\nresult = guard.verify_query(\"SELECT * FROM orders WHERE user_id = 'admin'' OR ''1''=''1'\")\nprint(f'Verified: {result[\"verified\"]}')\nprint(f'Message: {result.get(\"message\", \"\")}')"
}

STDOUT:
Verified: False
Message: Potential SQL injection detected

Execution completed successfully.

SELECT * FROM orders WHERE user_id = ?
-- Pass user_id as parameter

{
  "code": "from qwed_legal import LegalGuard\nimport hashlib\n\nguard = LegalGuard(provenance_config={'require_disclosure': True})\n\n# Deadline check\nd = guard.verify_deadline('2026-01-15', '90 business days', '2026-05-01')\nprint(f'Deadline verified: {d.verified}')\n\n# Liability check\nl = guard.verify_liability_cap(5000000, 200, 10000000)\nprint(f'Liability verified: {l.verified}')\n\n# Provenance check\ncontent = 'This AI-generated contract review...'\nh = hashlib.sha256(content.encode()).hexdigest()\np = guard.verify_provenance(content, {'content_hash': h, 'model_id': 'gpt-4', 'generation_timestamp': '2026-03-25T10:00:00+00:00'})\nprint(f'Provenance verified: {p[\"verified\"]}')",
  "background": true
}

Verification order is being placed for the request a1b2c3d4-e5f6-7890-abcd-ef1234567890. Check back using the 'verification_status' tool.

{
  "name": "verification_status",
  "arguments": {
    "job_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
  }
}

Status: success

Result:
STDOUT:
Deadline verified: True
Liability verified: True
Provenance verified: True

Execution completed successfully.

from langchain_anthropic import ChatAnthropic
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain.tools import StructuredTool
import subprocess
import json

# Create a tool that calls execute_python_code via MCP CLI
def run_verification(code: str) -> str:
    """Execute Python verification code via QWED-MCP."""
    result = subprocess.run(
        ["qwed-mcp-cli", "execute_python_code",
         "--code", code],
        capture_output=True, text=True
    )
    return result.stdout

verify_tool = StructuredTool.from_function(
    func=run_verification,
    name="execute_python_code",
    description="Execute Python code to verify calculations using QWED SDKs."
)

# Create agent with verification
llm = ChatAnthropic(model="claude-3-sonnet")
agent = create_tool_calling_agent(llm, [verify_tool], prompt)
executor = AgentExecutor(agent=agent, tools=[verify_tool])

# Use it
result = executor.invoke({
    "input": "What's the integral of 2x? Write a script to verify your answer."
})

User asks → AI calculates → AI writes verification script → AI responds
                             ↓ (if fails)
                         AI recalculates

"I calculated X. Let me verify this is correct...
[Runs verification script]
✅ Verified using deterministic computation.
The answer is definitely X."