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POST
/
v1
/
test
curl -X POST https://api.leanmcp.com/v1/test \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "mcp_id": "mcp_abc123def456",
    "message": "Send an email to john@example.com saying hello",
    "ai_model": "claude-sonnet-4-5",
    "context": {
      "user_id": "user_123"
    }
  }'

{
  "success": true,
  "data": {
    "ai_response": "I've sent an email to john@example.com with the subject 'Hello' and a friendly greeting message.",
    "tools_used": ["send_email"],
    "resources_accessed": ["user://contacts"],
    "execution_time": 2.3,
    "success_rate": 1.0
  },
  "meta": {
    "request_id": "req_test_789",
    "timestamp": "2023-12-01T12:00:00Z"
  }
}
Test how AI agents interact with your MCP server.

​
Request

​
mcp_id
string
required
ID of the MCP server to test
​
message
string
required
Message to send to the AI agent
​
ai_model
string
AI model to use for testing. Options: claude-sonnet-4-5, gpt-5.2, gpt-5.2 Default: claude-sonnet-4-5
​
context
object
Additional context for the AI agent

​
Response

​
success
boolean
Whether the test completed successfully
​
data
object
curl -X POST https://api.leanmcp.com/v1/test \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "mcp_id": "mcp_abc123def456",
    "message": "Send an email to john@example.com saying hello",
    "ai_model": "claude-sonnet-4-5",
    "context": {
      "user_id": "user_123"
    }
  }'

{
  "success": true,
  "data": {
    "ai_response": "I've sent an email to john@example.com with the subject 'Hello' and a friendly greeting message.",
    "tools_used": ["send_email"],
    "resources_accessed": ["user://contacts"],
    "execution_time": 2.3,
    "success_rate": 1.0
  },
  "meta": {
    "request_id": "req_test_789",
    "timestamp": "2023-12-01T12:00:00Z"
  }
}

​
Testing Best Practices

​
Start Simple

Test basic tool usage first: 
{
  "mcp_id": "your-mcp-id",
  "message": "What tools do you have available?",
  "ai_model": "claude-sonnet-4-5"
}

​
Test Edge Cases

Try confusing or ambiguous requests: 
{
  "mcp_id": "your-mcp-id", 
  "message": "Do that thing with the data",
  "ai_model": "claude-sonnet-4-5"
}

​
Test Complex Workflows

Chain multiple tool calls together: 
{
  "mcp_id": "your-mcp-id",
  "message": "Check my contacts, find John's email, and send him a meeting invite for tomorrow at 2pm",
  "ai_model": "claude-sonnet-4-5"
}

​
Interpreting Results

​
Success Rate

  • 1.0: All tool calls worked perfectly
  • 0.8-0.9: Mostly successful, minor issues
  • 0.5-0.7: Some failures, needs improvement
  • < 0.5: Major issues, review tool descriptions

​
Common Issues

  • Wrong tools used: Tool descriptions too similar
  • Missing tools: AI needs tools that don’t exist
  • Failed calls: Input validation or execution errors
  • No tools used: AI didn’t understand the request