Examples & Tutorials

Real-world examples of using QWED-MCP for verification in different scenarios.

---

Example 1: Financial Calculations

Scenario

You're building an AI-powered financial advisor. Users ask for compound interest calculations, and you need to ensure the AI's math is correct.

Setup

{
  "mcpServers": {
    "qwed-verification": {
      "command": "qwed-mcp"
    }
  }
}

Conversation

User: Calculate the future value of $10,000 invested at 5% annual interest, compounded monthly, for 10 years.

Claude's Process:

1. Calculate: FV = P(1 + r/n)^(nt) = 10000(1 + 0.05/12)^(12×10)
2. Call verify_math to confirm:

{
  "expression": "10000 * (1 + 0.05/12)**(12*10)",
  "claimed_result": "16470.09",
  "operation": "evaluate"
}

Result: ✅ VERIFIED

Claude's Response: The future value is $16,470.09 (verified).

---

Example 2: Legal Reasoning

Scenario

An AI legal assistant needs to verify that conclusions follow from legal premises.

Conversation

User: Under contract law: If both parties agree and consideration is exchanged, a contract is formed. Alice agreed and paid Bob. Is there a valid contract?

Claude's Process:

1. Formalize the logic
2. Call verify_logic:

{
  "premises": [
    "if agreed and consideration then contract",
    "Alice agreed",
    "consideration exchanged"
  ],
  "conclusion": "contract formed"
}

Result: ✅ VERIFIED - The conclusion logically follows.

---

Example 3: Secure Code Review

Scenario

A developer asks AI to review their code for security issues.

Conversation

User: Review this login function:

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

Claude's Process:

1. Call verify_code:

{
  "code": "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",
  "language": "python"
}

Result:

❌ FAILED - Found 2 security issues
Issues:
  - Dangerous function call: eval()
  - Warning: String concatenation in SQL query detected

Claude's Response:

🚨 Security Issues Found:

1. SQL Injection - Direct string formatting in SQL query
2. Code Injection - Using eval() for password comparison

Recommended Fix:

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

---

Example 4: Research Paper Validation

Scenario

Verifying statistical claims in AI-assisted research paper review.

Conversation

User: The paper claims: "If the p-value < 0.05, we reject the null hypothesis. Our p-value was 0.03. Therefore, we reject the null hypothesis."

Claude's Process:

{
  "premises": [
    "if p_value less than 0.05 then reject_null",
    "p_value less than 0.05"
  ],
  "conclusion": "reject_null"
}

Result: ✅ VERIFIED

---

Example 5: SQL Query Validation

Scenario

An AI data analyst generates SQL queries. You need to ensure they're safe before execution.

Conversation

User: Get all orders for user input "admin' OR '1'='1"

Claude's Process:

1. Generate query
2. Call verify_sql before responding:

{
  "query": "SELECT * FROM orders WHERE user_id = 'admin'' OR ''1''=''1'",
  "allowed_tables": ["orders", "users", "products"]
}

Result:

❌ FAILED - Potential SQL injection detected
Issues:
  - Suspicious tautology pattern: 1=1
  - Potential injection characters detected

Claude's Response:

⚠️ Security Warning: The input appears to be a SQL injection attempt. I cannot generate this query safely.

Safe Alternative: Use parameterized queries:

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

---

Example 6: Integration with LangChain

Python Code

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

# Create QWED tools that call MCP server
def verify_math(expression: str, claimed_result: str, operation: str = "evaluate") -> str:
    """Verify mathematical calculations using SymPy."""
    result = subprocess.run(
        ["qwed-mcp-cli", "verify_math", 
         "--expression", expression,
         "--claimed_result", claimed_result,
         "--operation", operation],
        capture_output=True, text=True
    )
    return result.stdout

math_tool = StructuredTool.from_function(
    func=verify_math,
    name="verify_math",
    description="Verify mathematical calculations. Use after doing any math."
)

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

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

---

Best Practices

1. Always Verify Before Responding

User asks → AI calculates → AI verifies → AI responds
                            ↓ (if fails)
                        AI recalculates

2. Use Appropriate Tools

Task	Tool
Math/calculations	verify_math
Logic/reasoning	verify_logic
Code review	verify_code
SQL queries	verify_sql

3. Handle Verification Failures

When verification fails:

1. Acknowledge the error
2. Recalculate
3. Verify again
4. Explain the correction to user

4. Explain Verification to Users

"I calculated X. Let me verify this is correct...
✅ Verified using symbolic mathematics.
The answer is definitely X."